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4.1.5 Shear Strength
The shear strength represents the magnitude of shear force the tailings can withstand prior to
sliding movement, and is largely influenced by the mineralogy, grain size distribution, and fluid content
within the tailings (Vick 1990). The shear strength of tailings is comprised of the resistance to sliding due
to the internal structure (friction), and the cohesion. The shear strength is particularly important if the
tailings are to be utilized as a construction material. The shear strength of the tailings is determined
through laboratory drained direct shear and consolidated-undrained triaxial shear testing (Das 2005).
The laboratory test method for drained direct shear tests are detailed in ASTM D3080-11
Standard Test Method for Direct Shear Test of Soils Under Consolidated Drained Conditions (2011c). A
shear box is utilized for a direct shear test and consists of a tailings sample placed within the two stacked
shear box halves. Prior to the test, a normal force is applied to the top of two shear boxes and the sample
is given time to consolidate. A shear force is then applied horizontally, at a constant rate, and deformation
of the tailings sample is measured as failure occurs along the horizontal plane of the two shear box
halves. Normal and shear stresses determined from the drained direct shear test are utilized to calculate
the effective friction angle of the tailings.
The laboratory test method for consolidated-undrained triaxial tests is detailed in ASTM D4767-11
Standard Test Method for Consolidated Undrained Triaxial Compression Test for Cohesive Soils (2011b).
Prior to the test, a confining pressure is applied and the tailings sample is given time to consolidate. The
confining pressure is then increased, while the sample is not permitted to drain, until shear failure occurs.
The pore pressure within the sample is recorded as confining pressure increases. Total and effective
stresses determined from the consolidated-undrained triaxial test are utilized to calculate the friction angle
of the tailings.
For drained conditions, the excess pore pressure from loading is rapidly dissipated. Vick (1990)
and Volpe (1979) reported results from a number of laboratory tests on copper tailings and found that
they typically have low cohesion values, indicating a greater quantity of sand- than clay-sized particles; an
effective stress friction angle ranging between 30 and 37 degrees; and an effective stress equivalent to
the total stress, ranging between 0 and 750 kilopascal (kPa) for drained conditions (Vick 1990, Volpe
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1979). For undrained conditions, rapid loading creates excess pore water pressures that may not
dissipate. Copper tailings were found to have lower effective stresses and effective stress friction angles,
accounting for pore water pressure in undrained conditions (Vick 1990, Volpe 1979).
4.2 Geochemical Properties
The geochemical characteristics of copper tailings are dependent on the ore, processing method
and additives utilized to extract the copper, and the amount (degree and nature) of weathering the tailings
are anticipated to undergo after disposal (Kossoff et al. 2014). Acid mine drainage (AMD) can result from
the oxidation of sulfide minerals and will alter the surrounding environment (Dold and Fontbote 2002,
Hesketh et al. 2010, Sima et al. 2011). The formation of AMD is influenced by the availability of water, the
ability for oxygen to be distributed from the tailings, and the grain size distribution of the tailings (Smuda
et al. 2008). Tailings are fine grained and have a large surface area for sulfide oxidation (Moncur et al.
2005). The prevention of AMD requires an understanding and accurate prediction of the geochemical
process (Osanloo et al. 2008, Smuda et al. 2008).
The most common sulfide ore mineral is chalcopyrite (CuFeS ), and after the removal of copper,
2
pyrite becomes the sulfide mineral (FeS ). Once sulfide minerals become exposed to air and water,
2
oxidation begins, resulting in the formation of aqueous ions that can be transported through surface water
runoff or groundwater infiltration (Al and Blowes1999). The chemical equations for the oxidation of pyrite
are shown as Equations 4.3 and 4.4 (USEPA 1994b, Moncur et al. 2005, Martin et al. 2008).
2FeS (s) + 7O (g) + 2H O(l) = 2Fe2+(aq) + 4SO 2-(aq) + 4H+(aq) (4.3)
2 2 2 4
4Fe2+(aq) + O (g) + 4H+(aq) = Fe3+(aq) + 2H O (4.4)
2 2
The oxidation of sulfide results in the formation of iron, sulfate and hydrogen ions. An increase in
hydrogen ions causes the pH of the solution to decrease and become acidic, and additional metals may
be dissolved (McGregor and Blowes 2002, Acero et al. 2007). Carbonate minerals such as calcium
carbonate (CaCO ) may be present within or added to the tailings to assist with neutralization of the acidic
3
solution (Dold and Fontbote 2002, Martin et al. 2008). The chemical equation for the neutralization of
oxidized pyrite is shown as Equation 4.5 (Moncur et al. 2005, Martin et al. 2008).
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Fe3+(aq) + 2SO 2-(aq) + H+(aq) + 2CaCO (s) + 5H O = Fe(OH) (s) + 2CaSO 2H O(s) + 2CO(g) (4.5)
4 3 2 3 4 2
Materials utilized to construct a tailings disposal facility should be selected to minimize chemical
reactions that cause AMD, and the facility should be constructed to minimize water and oxygen infiltration
into the tailings (Sima et al. 2011). Installation of a cover over the tailings, or disposal of the tailings
beneath water, can prevent water and air infiltration. Separating the sulfide minerals from the tailings prior
to disposal can also prevent the formation of AMD (Hesketh et al. 2010). The following geochemical
properties of the tailings must be defined prior to selection of a disposal technology:
Mineralogy
Acid Generation Potential
Net Acid Generation pH
Leachability
Rate of Oxidation
4.2.1 Mineralogy
The composition of the copper tailings must be defined to predict what minerals may potentially
become soluble or oxidized (MEND 1991, USEPA 1994b). Mineralogical testing is performed to identify
the minerals present within the copper tailings. X-ray diffraction, optical microscopic, and electron
microscope are a few analyses that can be utilized to determine the mineral composition of the tailings.
The mineralogical and acid base accounting (ABA) analyses results are utilized to identify the acid
generation potential (AGP) and acid neutralization potential (ANP) of the tailings (USEPA 1994b).
4.2.2 Acid Generation Potential
The acid generation potential (AGP) is utilized to define the probability of the copper tailings to
generate acid. Acid base accounting (ABA) is a static test and, with mineralogical test results, is utilized to
identify the AGP and ANP of the copper tailings. The AGP is a measurement of total, soluble, extractable
and residual sulfide present within the tailings (USEPA 1994b). The ANP is measured from the amount of
carbonate minerals present within the tailings to neutralize that addition of acid (USEPA 1994b). The
tailings are considered acid generating if the AGP is greater than the ANP (MEND 1991).
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4.2.3 Net Acid Generation pH
Net acid generation (NAG) pH test results are utilized in addition to the ABA test results to
classifying the AGP of a sample. Acid generation and neutralization reactions are forced to occur
simultaneously during the NAG pH test (Miller et al. 1997). The net result of the acid generation and
neutralization reactions provides a total measurement of the amount of acid generated. The pH
measurement is recorded, and a pH of less than 4.5 indicates the sample is potentially NAG.
4.2.4 Leachability
A short-term leach test is performed utilizing the USEPA’s Synthetic Precipitation Leaching
Procedure (USEPA 1994a) to simulate precipitation and runoff that can occur with a tailings sample.
Metals with the potential to leach into the environment are identified. If the tailings are anticipated to be
coarse sand with minimal fines, a meteoric water mobility procedure (MWMP) rapid leach test can be
substituted.
4.2.5 Rate of Oxidation
A humidity cell test is utilized to determine the rate of pyrite oxidation in a humid environment,
and is intended to simulate weathering that can occur during periods of dry and wet air flow (MEND
1991). Test results are utilized to determine acid generation rates (EPA 1994b) and variations in the
concentrations of metals within the tailings for the test duration (MEND 1991).
4.3 Design Considerations
The following design considerations must be addressed prior to selection of a disposal
technology, and are influenced by the geotechnical and geochemical characteristics of the tailings
(Kujawa 2011, Barerra and Caldwell 2015):
Surface Water and Groundwater
Capacity
Consolidation and Settlement
Stability
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Accounting for these geotechnical and geochemical considerations during the design phase of a mine is
more efficient approach than making adjustments after the mine is operational (Edraki et al. 2014).
4.3.1 Surface Water and Groundwater
The influence of surface water infiltration, seepage and runoff should be considered prior to the
selection of a tailings disposal technology. Surface water may infiltrate the tailings and can initiate a
series of chemical reactions that dissolves metals, as described above. Seepage through the tailings and
into the groundwater or surface water systems as AMD may occur, transporting the dissolved metals and
altering the surrounding environment (Dold and Fontbote 2002, Hesketh et al. 2010, Sima et al. 2011).
Surface water may also erode and carry fine tailings particles outside of the disposal facility footprint
(Franks et al. 2011). Additional water treatment processes may be required prior to discharge into the
environment (Al and Blowes 1999). Engineered control structures may be constructed to divert surface
water away from the disposal facility and only allow stormwater to come into contact with the tailings.
Engineered liner systems may also be constructed to encapsulate the disposed tailings and prevent
seepage into the environment (Barrera et al. 2011). Tailings may also be disposed under water to prevent
sulfide oxidation (Acero et al. 2007). Accurate infiltration, seepage and runoff estimates must be achieved
and accounted for in the disposal technology design.
4.3.2 Capacity
A tailings disposal facility must be designed to a capacity that considers the volume of tailings
that will come out of the process facility. Both the total volume of fluids added during processing, and the
tailings produced must be accurately estimated prior to selection of a disposal technology. The capacity
of the disposal facility must be designed to accommodate the total volume. The ultimate design capacity
of a facility is driven by the amount of consolidation that may occur within the tailings (Gjerapic et al.
2008). Variations can occur during ore processing, producing tailings that may vary from the design
(Barrera et al. 2011). The tailings material availability can also vary, and may limit the construction
material available to meet design capacities.
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4.3.3 Consolidation and Settlement
Consolidation occurs as fluid is drawn out and removed from the tailings, reducing the total
volume of the tailings (Ritchie et al. 2009). The density of the tailings increases and settlement occurs as
the tailings consolidate. Poor consolidation of the tailings can occur if there is a high percentage of clay
sized particles present, inhibiting the drainage of fluid from the pore spaces, making settlement of the
tailings difficult to predict. If tailings are disposed of within an embankment, the rate of placement must be
maintained to not increase pore pressure, which would cause a decrease in strength of the underlying
tailings (Davies 2002).
4.3.4 Stability
A tailings disposal technology must maintain geochemical and geotechnical stability. Seepage
through the tailings, weathering, and erosion can result in both structural deformation of a disposal
technology (Kossoff et al. 2014) over time, and the formation of AMD through oxidation of sulfide minerals
(Dold and Fontbote 2002, Hesketh et al. 2010, Sima et al. 2011). An accurate prediction of the tailings’
geochemistry is required to prevent AMD formation and maintain long-term stability of the disposal
technology (Osanloo et al. 2008, Smuda et al. 2008). Consolidation of the tailings can occur over time as
fluid is drawn out and removed, and may result in settlement of the tailings. An accurate prediction of
tailings settlement is required to design and maintain long-term stability of the disposal technology.
A tailings disposal facility must maintain containment of the tailings during a seismic event
(Barrera et al. 2011). The frequency and duration of the seismic event and associated ground
accelerations are site-specific and should be considered during selection of the tailings disposal
technology (Barrera et al. 2011). It is also important to estimate the post-seismic strength of the tailings,
as a decrease in strength compared to pre-seismic strength is likely. Liquefaction can occur within
saturated tailings during seismic loading (Boulanger and Idriss 2006). The greater the degree of
saturation, the greater the likelihood of liquefaction occurring, resulting in deformation of the tailings and
the disposal facility (Barrera et al. 2011). Sand sized particles that are separated for construction of a
surface impoundment are typically unsaturated and are less likely to be liquefied under seismic loading
(Boulanger and Idriss 2006, Ritchie et al. 2009). Significant strains can develop during a seismic event,
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disturbance to the environment, and post-closure land use. For backfill tailings disposal technologies, the
location of the disposal facility should be selected considering the location of voids to be backfilled.
The distance between the process and disposal facilities will determine the distance the tailings
will need to be transported through a series of pumps and pipes, truck haulage, or conveyor (Kossoff et
al. 2014). Generally, tailings transport costs are higher for disposal facilities located further from the
processing facility, and lower for disposal facilities located near the processing facility. Placement of the
disposal facility downgradient from the processing facility will allow for utilization of gravity transport and
may decrease overall tailings transport costs. The disposal facility location must meet design capacities.
Surrounding footprint availability is desirable and may provide the facility space to expand into should
capacity requirements change.
A tailings disposal facility footprint should be selected to maintain a distance from water
resources, natural wildlife habitats, and sites of archaeological importance that will result in minimal
disturbance. The disposal facility footprint must also be located within the mine owner’s property
boundary and situated to not impact surrounding properties, existing utility corridors, and access roads.
Visibility of the disposal facility must also be considered when selecting a location. Post-closure land use
options should be considered and closure plans developed that attempt to restore the disposal facility
footprint to pre-mine conditions.
5.1.2 Climate
Average and extreme climate conditions can influence the selection of a disposal technology
(Dold and Fontbote 2001). Temperature averages, seasonal variations, and extremes are important for
water management. Water evaporation increases with increasing temperature, and can result in a
shortage of water available to meet ore processing demands. Alternatively, low temperatures can induce
freezing and result in a shortage of available water. Water may be diverted from natural resources to
make up this deficit, however such withdrawals will likely be subject to existing downstream water rights.
Precipitation averages, seasonal variations, and extremes are also important for water
management. Drought conditions can result in a shortage of water available. Alternatively, periods of
extreme precipitation or storm events can result in an excess of water available (Schoenberger 2016).
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Water, in addition to what is required by the mine, must be either stored or discharged into the
environment. Any discharge into the environment must meet regulatory requirements, and may require
treatment prior to discharge into the environment. Construction of an on-site water treatment facility must
be considered during selection of the disposal facility as this may influence the location selection and
disposal capacity available. Wind and water erosion during large storm events may also influence the
geotechnical and geochemical stability of the disposal facility, and transport tailings outside of the
disposal facility footprint.
5.1.3 Geology
The local geology determines what materials are readily available for use in construction of the
disposal facility (Kossoff et al. 2014). Local materials may be utilized for construction of the tailings
disposal facility containment embankment, foundations, or as additives to the tailings prior to backfill
placement. Knowledge of the surrounding mineralization is critical for selection of a disposal facility
footprint that will not impact future expansion potential of the mine. The location of geologic structures
such as faults may influence stability of both the disposal facility and the surrounding topography.
5.1.4 Surface Water and Groundwater
Existing surface water drainage basins can be altered with the placement of a tailings disposal
facility. Surface water may be diverted around a facility to minimize the potential for contact with the
tailings. The amount of water to be diverted increases as the size of the surface water basin increases.
Generally, the cost of surface water diversions is higher with larger basins. The location of groundwater is
important to estimate the potential for seepage into the groundwater system.
5.2 Impoundment Tailings Disposal Technology
Impoundments are the primary technology utilized for the disposal of a conventional tailings
slurry, although dewatered paste and thickened tailings may also be placed within the impoundment.
Embankments or dams are utilized to retain the tailings within the impoundment. Water and process fluids
are often stored on the top of the impoundment in a tailings pond, and decant systems are utilized to
control the fluid levels. Consolidation of the tailings forces fluids within pore spaces upward, also
contributing to the tailings pond fluid level. An impoundment can be designed to accommodate water
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supply fluctuations, and to recycle fluids back into the mining process as required. The tailings pond
minimizes the potential for tailings dust migration outside of the facility footprint. Perimeter fencing is
typically installed to prevent wildlife access to the tailings pond, and the placement of plastic balls (bird
balls) within the tailings pond act as a floating cover and may deter birds from landing on the pond.
5.2.1 Construction Methods
There are three methods for tailings impoundment construction that vary by embankment design
configuration, as illustrated in Figure 5.1: upstream, centerline, and downstream (Vick 1990). Each
configuration begins with the construction of a starter dam and then varies with subsequent raises. The
dams are typically built in stages utilizing waste rock or filtered sand tailings (Wei et al. 2009, Barrera and
Caldwell 2015, Schoenberger 2016).
The downstream construction method has progressive raises constructed overlying the starter
dam or previous raise, maintaining a uniform interior, upstream slope as depicted in Figure 5.1. This is
generally the most stable method for construction of subsequent raises, however it requires the greatest
quantity of material to construct. Greater material quantities equate to higher construction costs. The rate
of construction for the dam and the rate of tailings deposition is of less concern with the downstream
method, as an entirely new dam is constructed and the foundation is extended further downstream with
each raise. The tailings pond location, decant, and fluid recycling system will be moved with each
subsequent raise. The coarse tailings material settles closer to the dam, forming a beach between the
dam crest and the tailings pond, while the fine tailings settle within the pond footprint (Yin et al. 2011).
The centerline construction method has progressive raises constructed directly above the
centerline of the starter dam or previous raise as depicted in Figure 5.1. A portion of the subsequent raise
will overlay deposited tailings from the previous raise, making this method generally less stable than the
downstream construction method. Much of the subsequent raises will be constructed over the previous
raise, and will extend slightly downstream. This is a lower cost configuration as it requires lower material
quantities for construction than the downstream method. The rate of construction for the dam and the rate
of tailings deposition is of greater concern with the centerline method, as a portion of the subsequent dam
will be constructed overlying the disposed, potentially unconsolidated tailings. The tailings pond location,
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decant, and fluid recycling system will not move with each subsequent dam raise. Like the downstream
construction method, a coarse tailings beach forms between the dam crest and the tailings pond, while
the fine tailings settle within the pond footprint (Yin et al. 2011).
Figure 5.1 Generalized cross section of the downstream, centerline, and upstream configurations for
construction of the impoundment tailings disposal technology. Adapted from Vick (1990) and the State
Government of Victoria (2016).
The upstream construction method has progressive raises constructed overlying both the tailings
and starter dam or previous raise, maintaining a uniform exterior, downstream slope as depicted in Figure
5.1. The subsequent raises progressively move upstream with a larger portion of the subsequent raise
overlying tailings from the previous raise, making this method less stable than both the downstream and
centerline methods (Yin et al. 2011, Kossoff et al. 2014). There is no downstream extension of the
impoundment footprint required with the upstream method, and it is generally the least costly option,
requiring the lowest amount of construction quantities. The rate of construction for the dam and the rate of
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tailings deposition is of greatest concern with the upstream method, as a larger portion of the subsequent
dam will be constructed overlying the disposed, likely unconsolidated tailings. This method is not typically
utilized in modern construction due to the reliance on the disposed tailings as a structural foundation. Like
the downstream and centerline construction method, a coarse tailings beach forms between the dam
crest and the tailings pond, while the fine tailings settle within the pond footprint (Yin et al. 2011).
5.2.2 Disposal Technology Considerations
Stored energy and water within the impoundment must be considered during the design,
construction, operation, and closure of the tailings impoundment (Franks et al. 2011). Should failure of the
impoundment or seepage through the foundation and the facility occur, the tailings and fluids will mobilize
and extend beyond the design footprint, increasing the footprint of environmental influence (Vick 1990,
Kossoff et al. 2014). Reducing the amount of ponded water on top of the impoundment and dewatering
the tailings can lower the potential failure consequences. However, the tailings may become oxidized and
mobilize as dust if not submerged (Kossoff et al. 2014). Reducing the slope angle of the dam will increase
stability of the impoundment, but will also result in an increase in construction material requirements and
associated costs. Where water is scarce (arid climates), reducing the tailings pond and dewatering the
tailings may be favorable (Franks et al. 2011). Alternatively, where water is abundant (humid climates),
maintaining the tailings pond, dewatering the tailings, and reducing the slope angle of the dam may be
favorable.
Stability of the impoundment must also be considered during design, construction, operation and
closure (Grangeia et al. 2011). Weak zones within the foundation footprint should be characterized and
potentially removed prior to dam construction and tailings disposal (Vick 1990, Franks et al. 2011, Kossoff
et al. 2014). The slope angle of the dam should be designed to maintain stability while retaining the
disposed tailings, and consider the cost of construction materials. Vertical drains can be installed to
dissipate excess pore pressure within the tailings, and may be considered in areas of high seismic activity
where the additional material cost is offset by the benefits (Obermeyer and Alexieva 2011). Consolidation
of the tailings can occur, resulting in surface deformation that may limit post-mine land use options. Slope
armoring may be employed where erosion of the dam slope is a concern.
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5.3 Submarine Placement Tailings Disposal Technology
Submarine placement disposal technology is utilized for the disposal of a conventional tailings
slurry. The submarine disposal technology is limited to facilities located near a water source, and is not a
typically preferred method for tailings disposal due to environmental and social influences. If employed,
tailings disposal utilizing submarine placement should be deep enough to avoid influencing the shallow
aquatic environment (Franks et al. 2011).
Submarine tailing disposal may be considered in areas of high seismicity, humid climates where
oxidation and precipitation are difficult to control, or where land for surface disposal is not available
(Franks et al. 2011). Disposal costs are relatively low compared to other disposal methods, however there
can be unforeseen environmental remediation costs. Tailings containment is not monitored or maintained
through submarine placement, and the reactions with the ocean environment are not fully understood
(Franks et al. 2011).
5.4 Open Pit Backfill Tailings Disposal Technology
Open pit backfill technology is utilized for the disposal of conventional tailings slurry, although
dewatered paste and thickened tailings may also be placed as backfill. The tailings are utilized to stabilize
the existing pit slopes and minimize the visual influence of the open pit once mining is complete. A pit
lake may form above the tailings from surface water or groundwater flow as shown in Figure 5.2.
Perimeter fencing is typically installed to prevent wildlife access to the tailings pond.
Mining within the pit must be complete prior to placement of the tailings backfill. Ore processing
and the production of tailings will coincide with mine excavation during operation. Temporary storage of
the tailings prior to backfill placement will need to be considered, and may result in environmental
disturbance within the temporary disposal facility footprint and additional costs (Franks et al. 2011). A
staged schedule for mine excavation could be implemented to delay processing of the ore, though this
could result in additional ore stockpile construction and may not be economical for large mine operations.
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Figure 5.2 Generalized cross section of the open pit backfill tailings disposal technology.
As with the impoundment disposal technology, consolidation of the tailings can occur with open
pit backfill and may result in surface deformation. The potential for surface deformation limits the post-
mine land use options. Future mine expansions may be limited should backfill be placed where access to
mineralization that extends beyond the current pit footprint is covered with tailings backfill. The potential
for groundwater contamination is also a concern, and knowledge of the geochemical composition of the
tailings and location of groundwater is required to prevent contamination. A geomembrane liner may be
installed within the pit, prior to tailings backfill placement, to minimize seepage potential. However, steep
pit slopes and additional material cost may limit the potential for geomembrane liner installation.
5.5 Tailings and Waste Rock Combined Placement Disposal Technology
Tailings and waste rock combined placement technology is utilized for the disposal of dewatered
paste and thickened tailings with waste rock in a single stockpile disposal facility. The combination of
waste materials reduces the footprint required for construction of two separate facilities, though the
footprint of a single facility will be slightly larger to accommodate both waste volumes. The general
concept is that the fine tailings particles will fill the void spaces present within larger waste rock material
(Barrera and Caldwell 2015).
5.5.1 Construction Methods
There are two methods for tailings and waste rock combined placement that vary by deposition
as illustrated in Figure 5.3: co-disposal and co-mingled. The combined waste rock and tailings are
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stockpiled above an engineered foundation and do not require the construction of additional containment
structures.
Figure 5.3 Generalized cross section of the tailings and waste rock combined placement disposal
technology.
The co-disposal construction method includes placement of tailings and waste rock materials in
alternating layers within a single stockpile (Barrera and Caldwell 2015) as depicted in Figure 5.3. In
practice this is less likely to have continuous, alternating tailings and waste rock layers that span the
extent of the stockpile as shown, and will be layered based on the availability of waste material for
placement. The fine tailings placement is restricted to areas toward the center of the stockpile and the
waste rock should be placed along the perimeter to contain the finer tailings. Tailings and waste rock
segregation is likely to occur within a co-disposal facility.
The co-mingled construction method includes mixing of the tailings and waste rock prior to
placement as a single material in a single stockpile (Barrera and Caldwell 2015) as depicted in Figure 5.3.
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The overall mine disturbance footprint may increase with construction of a separate mixing facility, though
construction of two separate disposal facilities, one for tailings and one for waste rock, will likely have an
overall larger footprint than a co-mingled facility and mixing plant. Consolidation of the co-mingled facility
is likely to occur at a faster rate than the co-disposal facility as small tailings particles fill the void spaces
of larger waste rock particles during mixing. Decreasing the void space will decrease the overall volume
placed within the disposal facility and may result in a smaller design footprint required for a co-mingled
facility.
5.5.2 Disposal Technology Considerations
Co-disposal and co-mingling technologies can reduce the overall footprint required for the
construction of separate waste rock and tailings disposal facilities. Co-mingling can reduce the potential
for oxidation, infiltration, and AMD by filling void spaces typically present within the waste rock with
tailings material (Barrera and Caldwell 2015). As with the impoundment and dry stack disposal
technologies, stability of the stockpiles must be considered during design, construction, operation, and
closure (Grangeia et al. 2011). Weak zones within the foundation footprint should be characterized and
potentially removed prior to tailings disposal (Vick 1990, Franks et al. 2011, Kossoff et al. 2014). The
slope angle should be designed to maintain stability.
5.6 Underground Backfill Tailings Disposal Technology
Underground backfill technology is utilized for the disposal of dewatered paste and thickened
tailings. The tailings provide stabilization of underground voids as underground structural support (Franks
et al. 2011), and can potentially eliminate the need for surface disposal facilities. The placement of
underground backfill must be considered in the overall mine schedule (Schoenberger 2016). Ideally,
backfill with thickened tailings would occur immediately after ore processing and mineral extraction, and
underground excavation has advanced beyond the backfill placement location. Similarly, backfill with
paste tailings would occur immediately after ore processing, mineral extraction, and additives have been
mixed, and underground excavation had advanced beyond the backfill support placement location.
A surface disposal facility may still be required depending on the material balance between the
underground excavation backfill placement requirements and the paste or thickened tailings generated. If
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a balance is not achieved, a temporary surface tailings storage facility will need to be constructed creating
an environmental disturbance. Similar to the open pit backfill disposal technology, knowledge of the
geochemical composition of the tailings and location of groundwater is required to prevent contamination
of the groundwater system (Tariq and Yanful 2013).
Dewatered paste tailings can be engineered with additives to achieve a design strength required
for underground support systems, while thickened tailings are typically utilized to backfill voids. However,
economic concerns include the cost to achieve the design criteria utilizing additives. Dewatered paste
tailing placement as underground support backfill requires specific quantity mixtures of tailings with
aggregates and cement (Tariq and Yanful 2013). The curing time for cemented paste backfill is typically
longer than concrete, and the physical properties of cured backfill are between soil and soft rock (Tariq
and Yanful 2013). Seepage through the cemented paste backfill is less than seepage through an
uncemented backfill, resulting in a decreased potential for metal leaching.
5.7 Dry Stack Tailings Disposal Technology
Dry stack technology is utilized for the disposal of filtered tailings. Dry stack tailings disposal does
not require construction of embankments to retain the tailings, and the tailings are instead stacked into a
stockpile. Dry stack disposal facilities generally have smaller footprints than conventional slurry
impoundments due to the removal of fluids prior to disposal (Schoenberger 2016). A low-permeability
foundation is often required to support the overlying stockpile and prevent residual fluid or stormwater
infiltration from seeping through the stockpile foundation.
Dry stack tailings facilities can be utilized in arid environments, areas of high seismicity, and
where materials are not present to construct dams required for an impoundment. Maintaining consistency
of the filtered tailings to meet the design requirements can be a challenge due to inconsistencies within
the ore processing and mineral extraction methods. The elimination of ponded water associated with
impoundments reduces the consequence of failure and environmental influences (Franks et al. 2011,
Schoenberger 2016). However, an additional facility may be required to store processing fluids in the
absence of a tailings pond. There is a potential for environmental contamination outside of the facility
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footprint through tailings dust migration (Kossoff et al. 2014) and slope erosion, and the high processing
costs to dewater the filtered tailings must be considered (Shoenberger 2016).
As with the tailings impoundment disposal technology, stability of the dry stack must also be
considered during design, construction, operation and closure (Grangeia et al. 2011). Weak zones within
the foundation footprint should be characterized and potentially removed prior to tailings disposal (Vick
1990, Franks et al. 2011, Kossoff et al. 2014). The slope angle of the facility should be designed to
maintain stability, and slope armoring may be employed where erosion of the dry stack slope is a
concern. The potential for surface deformation is less than with impoundments.
5.8 Other Technologies
Other tailings disposal technologies that are not yet widely applied include the utilization of
tailings as additives for structural fill and construction material. The quantity of tailings produced currently
exceeds the amount that can be reused, and traditional tailings disposal technologies are still required.
Asphalt mixtures have been tested where copper tailings are utilized as a substitute for aggregates and
have met design standards equivalent to a non-substitute aggregate mix (Oluwasola et al. 2015).
Concrete mixtures have also been tested where copper tailings are utilized as additives and have
achieved similar compressive and tensile strength as concrete without tailings additives (Onuaguluchi and
Eren 2012). Ongoing research and laboratory testing for the use of copper tailings in the manufacturing
of construction bricks is being conducted at the University of Arizona. Test results have indicated that
copper tailings can be utilized to produce bricks that meet equivalent standards as the conventional
bricks (Ahmari and Zhang 2012).
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CHAPTER 6
QUALITATIVE RISK ASSESSMENT OF TAILINGS DISPOSAL TECHNOLOGIES
A qualitative risk assessment of current copper tailings disposal technologies was performed to
identify key drivers for consideration during the selection of each technology, relative to the other
available technologies. The assessment was divided into two parts to evaluate risk from: 1) the
environmental, social, and economic elements described in Chapter 2; and 2) the geotechnical,
geochemical and site-specific design elements described in Chapters 4 and 5. Risk is ranked critical,
high, moderate, or low for each element, based on the likelihood and severity of the element should be
considered during the selection of a tailings disposal technology, as shown in Table 6.1.
Table 6.1. Risk matrix for assessment of the likelihood and severity of an element to influence selection of
a disposal technology.
Severity
Likelihood Negligible Minor Marginal Major
(1) (2) (3) (4)
Always Low Moderate High Critical
(4) (4) (8) (12) (16)
Likely Low Moderate Moderate High
(3) (3) (6) (9) (12)
Unlikely Low Low Moderate Moderate
(2) (2) (4) (6) (8)
Rare Low Low Low Low
(1) (1) (2) (3) (4)
A critical rank implies the element will always be a major driver for consideration during the
selection of a tailings disposal technology. A high rank implies the element will either always be a
marginal driver, or likely be a major driver for consideration during the selection of a tailings disposal
technology. A moderate rank implies the element will either always or likely be a minor driver, likely or
unlikely be a marginal driver, or unlikely be a major driver for consideration during the selection of a
tailings disposal technology. A low rank implies either the severity is negligible or the likelihood of
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occurrence is rare for an element to be a driver for consideration during the selection of a tailings disposal
technology. A rank of variable was assigned to an element with site-specific dependencies that limit
qualitative assessment of both likelihood and severity of the element. For this risk assessment, elements
with a critical, high, and variable rank are key drivers for consideration during the selection of a tailings
disposal technology.
6.1 Environmental, Social, and Economic Elements
A qualitative risk assessment was performed to identify key environmental, social, and economic
drivers for consideration during the selection of a tailings disposal technology. Environmental elements
utilized for the assessment include land disturbance, water disturbance, and area contamination resulting
from dust, noise, and carbon dioxide (CO ) emissions. Social elements utilized for the assessment
2
include imposed cost, public health, public safety, political and regulatory climate, and aesthetics and land
use. Economic elements utilized for the assessment include costs associated with processing,
construction, temporary storage, and potential failure; investor confidence; ground conditions; and
resource extents.
6.1.1 Qualitative Risk Assessment – Environmental Elements
A summary of the qualitative risk assessment for environmental elements is presented in Table
6.2. Environmental elements utilized for the assessment include land disturbance, water disturbance, and
area contamination resulting from dust, noise, and carbon dioxide (CO ) emissions.
2
Land availability for construction and disturbance to the natural ground topography within the
footprint of a tailings disposal facility relates to the size required for tailings disposal. Technologies that
involve tailings disposal above the natural ground topography (i.e. impoundment, co-disposal, co-mingle,
dry stack) result in greater surface disturbance. A critical rank was assigned, indicating that land
availability and disturbance inside the facility footprint is a key driver for consideration during selection of
a surface disposal technology. The submarine placement tailings disposal technology will alter the natural
ground topography of the sea floor, and was assigned a moderate rank. Technologies that involve pit or
underground tailings disposal (i.e. open pit backfill and underground backfill) have minimal surface
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disturbance, and were assigned a low rank. Tailings placed as open pit or underground backfill may
decrease the potential for ground subsidence and restore the area to pre-mine ground topography.
Land availability for construction and disturbance to the natural ground topography outside of the
footprint of a tailings disposal facility relates to the potential for the tailings to move after placement, and
expansion of the facility beyond the initial footprint to accommodate greater tailings disposal quantities.
The submarine placement and downstream impoundment disposal technologies were assigned a high
rank, indicating land disturbance outside of the facility footprint is a key driver for consideration during
selection of both disposal technologies. The centerline impoundment disposal technology will have some
disturbance beyond the initial facility footprint resulting from subsequent raises to accommodate greater
disposal quantities, and was assigned a moderate rank. All other technologies (i.e. upstream
impoundment, open pit backfill, co-disposal, co-mingle, underground backfill, dry stack) will have minimal
disturbance outside of the facility footprint, and were assigned a low rank.
Disturbance to the submarine, surface water, and groundwater environments within and outside
of the footprint of a tailings disposal facility can occur with all tailings disposal technologies. Submarine
placement was assigned a high rank, indicating water disturbance within and outside of the facility
footprint is a key driver for consideration during selection of the disposal technology, due to the direct
disposal of tailings into a marine environment and the uncertainty of potential physical and chemical
interactions (Franks et al. 2011). Surface disposal technologies (i.e. impoundment, co-disposal, co-
mingle, dry stack) typically require construction of surface water diversions to minimize contact with the
tailings and to prevent erosion of the disposal facility, resulting in alteration of existing drainage paths,
and were assigned a moderate rank. Open pit and underground backfill technologies may alter
groundwater levels and chemistry, and were also assigned a moderate rank.
Area contamination by dust may occur with the absence of water and moisture within the tailings,
and in both arid and high wind environments. The dry stack placement disposal technology was assigned
a high rank, indicating dust is a key driver for consideration for selection of the disposal technology, due
to the high level of dewatering the tailings will undergo prior to placement (Edraki et al. 2014). Surface
disposal technologies (i.e. impoundment, co-disposal, co-mingle) are susceptible to dust generation and
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were assigned a moderate rank. Submarine placement, open pit backfill, and underground backfill are not
likely to produce dust contamination and were assigned a low rank.
Area contamination by both noise and by carbon dioxide (CO ) is largely attributed to the
2
equipment required for construction and operation of the disposal facility, and the equipment required for
transport of the tailings to the disposal facility. Surface disposal technologies (i.e. impoundment, co-
disposal, co-mingle, dry stack) were assigned a moderate rank for noise and CO contamination. In
2
general, submarine placement, open pit backfill, and underground backfill disposal technologies require
less equipment for construction and operation of the disposal facility, and were assigned a low rank.
6.1.2 Qualitative Risk Assessment – Social Elements
A summary of the qualitative risk assessment for social elements is presented in Table 6.3. Social
elements utilized for the assessment include imposed cost, public health, public safety, political and
regulatory climate, and aesthetics and land use.
Unanticipated costs can be imposed on the local community if a mine operation is suspended
(Franks et al. 2011), resulting in a potential reallocation of funds away from other community services.
Tailings disposal facilities that are left unattended are susceptible to failure, and the costs to remediate
the facility and impacted areas may be placed directly on the community. The impoundment tailings
disposal technology may result in greater cost to the community should a mine operation unexpectedly
cease, and was assigned a high rank, indicating that imposed cost is a key driver for consideration during
selection of the impoundment disposal technology. A moderate rank was assigned to the remaining
surface disposal technologies (i.e. co-disposal, co-mingle, dry stack). A low rank was assigned to the
submarine and backfill (i.e. open pit, thickened, paste) disposal technologies.
Ensuring the health and safety of the mine workers and members of the local community is
required to maintain a positive public perception and productive mine operation. All tailings disposal
technologies have the potential to adversely influence both the surface and groundwater systems relied
upon by the surrounding community. Surface disposal technologies (i.e. impoundment, co-disposal, co-
mingle, dry stack) in a greater impact on public health and safety, primarily resulting from the potential
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failure of the facility, and were assigned a moderate rank. A low rank was assigned to the submarine and
backfill (i.e. open pit, thickened, paste) disposal technologies.
The political climate will influence regulations that may govern tailings disposal and safe work
practices (Warhurst and Mitchess 2000). A variable rank was assigned for all tailings disposal
technologies, since the political climate is site-specific and varies globally, indicating that the political and
regulatory climate is a key driver for consideration during the selection of all tailings disposal
technologies.
Minimizing visual effects on the natural ground topography and planning for post-mine land use of
a tailings disposal facility may help to preserve the positive reputation of a mining company within the
community (Warhurst and Mitchess 2000). All surface disposal technologies have a visual effect on the
surrounding environment and require a period of facility monitoring after closure. The impoundment
tailings disposal technology typically has a longer period of settlement, which can delay the
implementation of post-mine land use, and a high rank was assigned, indicating that aesthetics and post-
mine land use is a key driver for consideration during selection of the impoundment disposal technology.
A moderate rank was assigned to the remaining surface disposal technologies (i.e. co-disposal, co-
mingle, dry stack). A low rank was assigned to the submarine and backfill (i.e. open pit, underground)
disposal technologies. Backfill within underground voids may be beneficial to reduce ground subsidence
effects on post-mine land uses.
6.1.3 Qualitative Risk Assessment – Economic Elements
A summary of the qualitative risk assessment for economic elements is presented in Table 6.4.
Economic elements utilized for the assessment include costs associated with processing, construction,
temporary storage, and potential failure; investor confidence; ground conditions; and resource extents.
The development of accurate and reliable cost estimates for each tailings disposal technology are
critical, though discrepancies may occur between the estimated and actual costs (Hansen 2007). In
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general, tailings processing costs increase as the level of dewatering increases. Tailings dewatering
requirements are greatest with the dry stack technology, and a high rank was assigned, indicating that
processing cost is a key driver for consideration during selection of the dry stack disposal technology. A
moderate rank was assigned to the co-disposal, co-mingle, and underground backfill disposal
technologies, and a low rank was assigned to the impoundment, submarine, and open pit backfill disposal
technologies.
The overall cost associated with construction of surface tailings disposal technologies (i.e.
impoundment, co-disposal, co-mingle, dry stack) is generally higher than the cost of submarine and
backfill disposal technologies. A high rank was assigned to the surface tailings disposal technologies (i.e.
impoundment, co-disposal, co-mingle, dry stack), indicating the cost of construction is a key driver for
consideration during selection of a surface disposal technology. A moderate rank was assigned to the
backfill technologies (i.e. open pit and underground), and a low rank was assigned to the submarine
technology.
Additional costs for temporary tailings storage prior to placement within a disposal facility are
generally associated with underground tailing disposal technologies (i.e. open pit backfill, underground
backfill). A high rank was assigned, indicating temporary storage cost is a key driver for consideration
during the selection of a backfill technology. Tailings may be stockpiled prior to mixing with waste rock
and placement in a co-mingle tailings disposal technology, and a moderate rank was assigned. A low
rank was assigned to the impoundment, co-disposal, dry stack and submarine tailings disposal
technologies.
Cost associated with the potential failure of a disposal technology are generally greater for
surface disposal facilities. A high rank was assigned to the impoundment disposal technology, where
failure would result in a release of both tailings and processing fluids into the surrounding environment,
indicating cost associated with potential failure is a key driver for consideration during the selection of the
impoundment disposal technology. A moderate rank was assigned to the co-disposal, co-mingle, dry
stack, and submarine placement disposal technologies, and a low rank was assigned to the backfill
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technologies (i.e. open pit, underground), where the tailings are confined to the space available for
backfill.
Investor confidence varies by project, location, and investor. Investors and other shareholders
must have metrics in place to ensure confidence maintains throughout a project, and confidence can be
lost if favorable tailings disposal practices are not in place (Franks et al. 2011). A variable rank was
assigned for all tailings disposal technologies, since investor confidence is site-specific and varies
globally, indicating that investor confidence is a key driver for consideration during the selection of all
tailings disposal technologies.
Variable ground conditions can influence the availability of facility construction material or
foundation material. A moderate rank was assigned to all surface tailings disposal technologies (i.e.
impoundment, co-disposal, co-mingle, dry stack), and a low rank was assigned to the submarine and
backfill (i.e. open pit, underground) tailings disposal technologies.
The resource extents must be delineated prior to selection of a disposal facility location to prevent
potential limitations to future expansion or additional disturbance of disposed tailings material. A low rank
was assigned to the submarine placement tailings disposal technology, since it is unlikely to influence
future mining activities. A moderate rank was assigned to all other tailings disposal technologies.
6.2 Qualitative Risk Assessment – Geotechnical and Geochemical Elements
A qualitative risk assessment was performed to identify key geotechnical and geochemical drivers
for consideration during the selection of a tailings disposal technology. Geotechnical and geochemical
elements utilized for the assessment include percent solids, strength additives, acid generation and
neutralization potential, consolidation and settlement, rate of construction relative to the rate of
placement, and stability. A summary of the qualitative risk assessment for geotechnical and geochemical
elements is presented in Table 6.5.
The percent solids indicates the level of dewatering and density of the disposed tailings (Davies
2011). Dewatered tailings with a higher percent solids tend to be more stable after placement. A high rank
was assigned to the impoundment, submarine placement, and open pit backfill technologies, since each
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facility accommodates placement of a conventional tailings slurry, indicating that the percent solids is a
key driver for consideration during the selection of these disposal technologies. A moderate rank was
assigned to the co-disposal, co-mingle, and underground backfill tailings disposal technologies, since
each facility accommodates placement of thickened or paste tailings. A low rank was assigned to the dry
stack tailings disposal technology, since the facility accommodates placement of filtered tailings.
Strength additives may be mixed with the tailings prior disposal to meet design specifications for
material performance. A moderate rank was assigned to the paste underground backfill tailings disposal
technology, since paste backfill is utilized as structural support within underground voids. A low rank was
assigned to all other tailings disposal technologies, since strength additives are not typically utilized.
The acid generation and neutralization potential of the tailings will provide an indication of the
potential for acid mine drainage (Dold and Fontbote 2002, Hesketh et al. 2010, Sima et al. 2011). A
critical rank was assigned to all tailings disposal technologies, due to the high potential for oxidation and
metal leaching, indicating that the acid generation and neutralization potential is a key driver for
consideration during the selection of all tailings disposal technologies.
Consolidation and settlement of the tailings is likely to occur as fluid is removed from the tailings
(Ritchie et al. 2009). A high rank was assigned to the impoundment tailings disposal technology, since the
conventional slurry has the greatest amount of fluid present and greater settlement of the tailings is
anticipated, indicating consolidation and settlement is a key driver for consideration during the selection of
the impoundment disposal technology. A moderate rank was assigned to the open pit backfill, co-
disposal, and co-mingle tailings disposal technologies, since settlement of the tailings can influence post-
mine land use options. A low rank was assigned to the underground backfill, dry stack, and submarine
placement technologies, since the thickened, paste, and filtered tailings have the least amount of fluids
present, and consolidation of the submarine placed tailings is unlikely to present a major settlement
concern.
The rate of construction relative to the rate of tailings placement must be maintained to minimize
undesired increases in pore pressure (Davies 2002). A high rank was assigned to the upstream
impoundment tailings disposal technology, since construction of a subsequent raise relies on the strength
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of the tailings from the previous raise, indicating that the rate of construction relative to the rate of
placement is a key driver for consideration during the selection of the upstream disposal technology. A
moderate rank was assigned to the centerline impoundment tailings disposal technology, since
construction of a subsequent raise partially relies on the strength of the tailings from the previous raise. A
low rank was assigned the downstream impoundment and all other tailings disposal technologies.
Stability of the tailings includes geochemical and geotechnical (static and seismic) stability, and
must be maintained to minimize potential failure of the disposal facility, maintain anticipated design
strength, and minimize acid mine drainage. A high rank was assigned to the upstream impoundment
tailings disposal technology. Fluid present within the tailings and construction of the facility on
unconsolidated, undrained tailings can have adverse effects on the stability, indicating that stability is a
key driver for consideration during the selection of the upstream disposal technology. A moderate rank
was assigned to all other surface tailings disposal technologies, and a low rank was assigned to the
submarine and backfill (open pit, underground) tailings disposal technologies.
6.3 Qualitative Risk Assessment – Site-Specific Elements
A qualitative risk assessment was performed to identify key site-specific drivers for consideration
during the selection of a tailings disposal technology. Site-specific elements utilized for the assessment
include location, climate, geology, and water. A summary of the qualitative risk assessment for site-
specific elements is presented in Tables 6.6 and 6.7.
6.3.1 Location
The transport distance for tailings from the plant to the disposal facility can vary for each disposal
technology. A low rank was assigned to the submarine placement tailings disposal technology, since a
requirement for this method is proximity to a submarine environment. A moderate rank was assigned to
all other tailings disposal technologies.
Capacity of the disposal facility must account for the tailings and process fluids. A critical rank
was assigned to the underground backfill tailings disposal technology, since the rate of excavation and
the tailings available for backfill must be balanced, indicating that capacity is a key driver for consideration
during the selection of the underground backfill disposal technology. If the quantity of tailings exceeds the
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volume available for placement, an additional disposal facility would be required. A moderate rank was
assigned to all surface disposal technologies (i.e. impoundment, co-disposal, co-mingle, and dry stack),
since they will be constructed to contain a specified disposal quantity. A low rank was assigned to the
submarine and open pit backfill tailings placement technologies, since the capacity available exceeds the
quantity of tailings for disposal.
Temporary storage may be required to meet pit or underground excavation and tailings
placement schedules. A critical rank was assigned to the open pit and underground backfill tailings
disposal technologies, since excavation must be complete prior to tailings backfill placement, indicating
that temporary storage is a key driver for consideration during the selection of the backfill disposal
technologies. A moderate rank was assigned to the co-mingle disposal technology, since temporary
stockpile placement may be required prior to mixing with the waste rock. A low rank was assigned to all
other tailings disposal technologies, since the tailings can be immediately placed.
The physical location of the disposal facility to water is important to minimize the transport
distance for the tailings. A critical rank was assigned to the submarine placement tailings disposal
technology, since the processing plant must be located near the submarine environment, indicating that
water is a key driver for consideration during the selection of the submarine disposal technology. A low
rank was assigned to all other tailings disposal technologies.
6.3.2 Climate
Temperature extremes can influence water management for a tailings disposal facility (Dold and
Fintbote 2001). A high rank was assigned to the impoundment tailings disposal technology. Water levels
managed at the disposal facility can be affected by temperature extremes, indicating that temperature is a
key driver for consideration during the selection of the impoundment disposal technology. A low rank was
assigned to all other tailings disposal technologies, since water is not managed directly at these facilities.
Precipitation extremes can also influence water management. A high rank was assigned to the
dry stack tailings disposal technology, since the potential for water to be added works against the
dewatering of the tailings, indicating precipitation is a key driver for consideration of the dry stack disposal
technology. A low rank was assigned to the submarine placement and underground backfill tailings
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disposal technologies, since disposal under water and underground is not likely to be influenced by
precipitation. A moderate rank was assigned to all other tailings disposal technologies, since precipitation
can influence the amount of water available to be managed.
Wind can transport tailings outside of the disposal facility footprint. A high rank was assigned to
the dry stack tailings disposal technology, since the dewatered tailings are susceptible to wind transport,
indicating that wind is a key driver for consideration of the dry stack disposal technology. A low rank was
assigned to the submarine and backfill (open pit, underground) disposal technologies, since disposal
below the natural ground topography is not susceptible to wind transport. A moderate rank was assigned
to all other tailings disposal technologies.
6.3.3 Geology
The local geology may affect the availability of materials for construction of the tailings disposal
facility and underlying foundation (Kossoff et al. 2014). A high rank was assigned to all surface tailings
disposal technologies (i.e. impoundment, co-disposal, co-mingle, dry stack), indicating that geology is a
key driver for consideration of the dry stack disposal technology. A low rank was assigned all other
disposal technologies, since construction of a disposal facility is not required.
Geologic structures located near or below the disposal facility may influence the stability of the
facility. A high rank was assigned to the impoundment tailings disposal technology, indicating that
structure is a key driver for consideration of the impoundment disposal technology. A moderate rank was
assigned to the remaining surface disposal technologies (i.e. dry stack, co-disposal, co-mingle), and a low
rank was assigned to the submarine placement and backfill (i.e. open pit, underground) technologies.
6.3.4 Water
Surface water diversions are typically constructed to minimize the potential for water coming into
contact with the tailings. A moderate rank was assigned to all surface tailings disposal technologies (i.e.
impoundment, co-disposal, co-mingle, dry stack), and a low rank was assigned to the submarine
placement and backfill (i.e. open pit, underground) tailings disposal technologies.
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The location of groundwater can influence the disposal facility’s foundation and potential for
seepage through the tailings. A low rank was assigned to the submarine placement tailings disposal
technology, since the tailings are submerged, and a moderate rank was assigned to all other tailings
disposal technologies.
6.4 Summary of Results
Elements were identified as critical, high, moderate, and low ranked drivers for the selection of
each tailings disposal technology. Critical and high ranked elements were identified as key drivers for
consideration during the selection of each tailings disposal technology. A summary of each technology is
included in the following sections.
6.4.1 Key Drivers
A summary of high and critical ranked elements, key drivers for consideration during the selection
of each tailings disposal technology, is presented in Table 6.8. Surface disposal technologies were
grouped together to identify common drivers and include the impoundment, tailings and waste rock, and
dry stack technologies. Other disposal technologies were also grouped together and include submarine
placement, open pit backfill, and underground backfill. The acid generation and neutralization potential,
political and regulatory climate, and investor confidence are key drivers for consideration during the
selection of all tailings disposal technologies.
Land disturbance, acid generation and neutralization potential, construction cost, and availability
of construction material are key drivers for consideration during selection of all surficial tailings disposal
technologies. Dust, processing cost, precipitation, and wind key drivers for consideration unique to the
selection of the dry stack disposal technology. There are a number of social, economic, geotechnical, and
site-specific key drivers unique to the selection of the impoundment disposal technology including
imposed costs on the local community, aesthetics and post-mine land use, cost associated with potential
facility failure, percent solids of the tailings, consolidation and settlement, temperature, and geologic
structure. The rate of facility construction and the rate of tailings placement, and stability are key drivers
for consideration unique to the upstream impoundment tailings disposal technology.
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Land and water disturbance, and location to water are key drivers for consideration unique to the
submarine placement tailings disposal technology. Facility capacity is a key driver for consideration
unique to the underground backfill disposal technology. Temporary tailings storage is a key driver for
consideration unique to the backfill (i.e. open pit, underground) technology.
6.4.2 Moderate Ranked Drivers
A summary of moderate ranked drivers for consideration during the selection of each tailings
disposal technology is presented in Table 6.9. Key drivers are shown in grey, while moderate ranked
drivers are highlighted yellow for each tailings disposal technology. Resource extents, transport distance,
and groundwater location are moderate ranked drivers for consideration during the selection of all
disposal technologies with the exception of the submarine placement technology. Surface disposal
technologies were grouped together to identify common drivers. Noise and CO pollution, public health
2
and safety, ground conditions, and diversions are moderate drivers for consideration during selection of
surface tailings disposal technologies.
6.4.3 Low Ranked Drivers
A summary of low ranked drivers for consideration during the selection of each tailings disposal
technology are presented in Table 6.10. Key and moderate ranked drivers are shown in grey, while low
ranked drivers are highlighted green for each tailings disposal technology. Surface disposal technologies
were grouped together to identify common drivers. In general, surface disposal technologies have higher
ranked environmental, social, economic, geotechnical, geochemical, and site-specific elements to
consider during the selection of disposal technologies.
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6.4.4 Impoundment Disposal Technology Drivers
Drivers for consideration during the selection of downstream, centerline, and upstream
impoundment tailings disposal technologies are depicted in Figures 6.1, 6.2, and 6.3, respectively.
Drivers were assigned an equivalent unit rating and grouped by key, moderate, and low ranks.
Environmental, social, economic, geotechnical, geochemical, and site-specific key drivers for
consideration include land disturbance, imposed cost, political and regulatory climate, aesthetics and
post-mine land use, cost associated with construction and potential failure, percent solids, acid generation
and neutralization potential, consolidation and settlement, temperature, local construction material source
availability, and local geologic structures.
The rate of construction relative to the rate of tailings placement, and overall stability of the facility
varies for the impoundment disposal technology. For the rate of construction relative to the rate of tailings
placement, a low rank is assigned to the downstream construction method, a moderate rank assigned to
the centerline construction method, and a high rank assigned to the upstream construction method. For
the overall stability of the facility, a moderate rank is assigned to the downstream and centerline
construction methods, and a high rank is assigned to the upstream construction method. The downstream
construction method with progressive raises overlying the starter dam or previous raise, will generally
result in a more stable configuration. The upstream construction method has progressive raises
constructed overlying both tailings and the starter dam or previous raise. Stability is influenced by the
underlying, likely unconsolidated tailings, and the rate of tailings placement relative to construction of the
subsequent raise.
6.4.5 Tailings and Waste Rock Disposal Technology Drivers
Drivers for consideration during the selection of co-disposal and co-mingle tailings and waste rock
disposal technologies are depicted in Figures 6.4 and 6.5, respectively. Drivers were assigned an
equivalent unit rating and grouped into high, moderate, and low ranks. Environmental, social, economic,
geochemical, and site-specific key drivers for consideration include land disturbance, political and
regulatory climate, cost associated with construction, investor confidence, acid generation and
neutralization potential, and local construction material source availability.
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The cost associated with and availability of space for temporary storage varies for the waste rock
and tailings disposal technology, with a low rating assigned to the co-disposal technology and a moderate
rating assigned to the co-mingle disposal technology.
6.4.6 Dry Stack Disposal Technology Drivers
Drivers for consideration during the selection of the dry stack tailings disposal technology are
depicted in Figure 6.6. Drivers were assigned an equivalent unit rating and grouped into high, moderate,
and low ranks. Environmental, social, economic, geochemical, and site-specific drivers for consideration
include land disturbance, area contamination from dust, political and regulatory climate, cost associated
with processing and construction, investor confidence, acid generation and neutralization potential,
precipitation, wind, and local construction material source availability.
6.4.7 Submarine Disposal Technology Drivers
Drivers for consideration during the selection of the submarine placement tailings disposal
technology are depicted in Figure 6.7. Drivers were assigned an equivalent unit rating and grouped into
high, moderate, and low ranks. Environmental, social, economic, geotechnical and geochemical, and site-
specific drivers for consideration include land disturbance, water disturbance, political and regulatory
climate, percent solids, acid generation and neutralization potential, and distance to water.
6.4.8 Open Pit Backfill Disposal Technology Drivers
Drivers for consideration during the selection of the open pit backfill tailings disposal technology
are depicted in Figure 6.8. Drivers were assigned an equivalent unit rating and grouped into high,
moderate, and low ranks. Social, economic, geotechnical, geochemical, and site-specific drivers for
consideration include political and regulatory climate, cost associated with and space available for
temporary storage, investor confidence, percent solids, and acid generation and neutralization potential.
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CHAPTER 7
CONCLUSIONS
7.1 Summary of Work
Regulations that apply to mining practices located within the United States, and International
guidance specific to tailings practices were identified, and their application to tailings disposal was
described. Environmental, social, and economic influences resulting from tailings disposal were outlined
and general goals for tailings disposal were listed. An overview of copper deposit geology, ore processing
methods, and resulting processed tailings properties was provided. Six tailings disposal technologies
were considered: impoundment, submarine placement, open pit backfill, tailings and waste rock
combined placement, underground backfill, and dry stack placement. Geotechnical and geochemical
properties of the tailings, and site-specific criteria were utilized to develop a list of considerations for
tailings disposal technology selection.
A qualitative assessment was performed for environmental, social, and economic influence
criteria; geotechnical, geochemical, and site-specific design consideration criteria; and the cost or each
tailings disposal technology. Results of the assessment indicate the impoundment tailings disposal
technology has the highest perceived risks and costs; tailings and waste rock combined, and dry stack
technologies have moderate perceived risks and moderate to high costs; and submarine, open pit, and
underground backfill have the lowest perceived risks and low to moderate costs. Recommendations for
future research concludes this thesis.
7.2 Recommendations for Future Research
Ongoing research should be performed to continuously identify efficient ore extraction, mineral
processing, and tailings disposal practices and technologies. The following recommendations for future
research have been identified:
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Verify the accuracy of the qualitative assessment performed through a quantitative analysis
of geotechnical, geochemical, and site-specific influences on the selection of a tailings
disposal technology, utilizing data obtained from multiple sources. Compare conclusions of
the quantitative analysis with those of this qualitative assessment. Select sources with
different processing methods to assess additional influences.
Verify the accuracy of the qualitative assessment performed through a quantitative analysis
of cost, utilizing data obtained from multiple sources. Include cost data for the design,
operation, closure, reclamation, and post-monitoring practices for tailings disposal. Select
sources from multiple facilities to assess cost variability.
Review existing closure strategies and develop site-specific closure criteria for each tailings
disposal technology.
Perform a detailed assessment for the use of tailings in structural applications, including
underground backfill and foundation construction. Identify potential strength additives and
develop a laboratory strength testing program. Outline ideal tailings properties for structural
application and review processing capabilities to meet the proposed design specifications.
Perform a detailed assessment of the tailings and waste rock combined disposal technology,
including the mechanics and feasibility of mixing and placement. Propose a laboratory-scale
mixing program to observe characteristics of varying amounts of waste rock and tailings.
Estimate the effort associated with a full-scale mixing program.
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REFERENCES
Acero, P, C. Ayora, and J. Carrera. “Coupled thermal, hydraulic and geochemical evolution of pyritic
tailings in unsaturated column experiments.” Geochimica et Cosmochimica Acta 71 (2007): 5325-
5338.
Al, T.A. and D. W. Blowes. “The hydrogeology of a tailings impoundment formed by central discharge of
thickened tailings: implications for tailings management.” Journal of Containment Hydrology
(1999): 38: 489-505.
Ahmari, S. and L. Zhang. “Production of eco-friendly bricks from copper mine tailings through
geopolymerization.” Construction and Building Materials (2012): 29: 323-331.
American Society for Testing and Materials (ASTM). Standard Test Methods for Particle-Size Distribution
(Gradation) of Soils Using Sieve Analysis. D6913-04 (2009).
American Society for Testing and Materials (ASTM). Standard Test Methods for Laboratory Determination
of Water (Moisture) Content of Soil and Rock by Mass. D2216-10 (2010a).
American Society for Testing and Materials (ASTM). Standard Test Methods for Liquid Limit, Plastic Limit,
and Plasticity Index of Soils. D4318-10 (2010b).
American Society for Testing and Materials (ASTM). Standard Practice for Classification of Soils for
Engineering Purposes (Unified Soil Classification System). D2487-11 (2011a).
American Society for Testing and Materials (ASTM). Standard Test Methods for Consolidated Undrained
Triaxial Compression Test for Cohesive Soils. D4767-11 (2011b).
American Society for Testing and Materials (ASTM). Standard Test Methods for Direct Shear Test of Soils
Under Consolidated Drained Conditions. D3080-11 (2011c).
American Society for Testing and Materials (ASTM). Standard Test Methods for Specific Gravity of Soil
Solids by Water Pycnometer. D854-14 (2014).
American Society for Testing and Materials (ASTM). Standard Test Methods for Measurement of
Hydraulic Conductivity of Saturated Porous Materials Using a Flexible Wall Permeameter. D5084-
16a (2016a).
American Society for Testing and Materials (ASTM). Standard Test Methods for Particle-Size Distribution
(Gradation) of Fine-Grained Soils Using Sedimentation (Hydrometer) Analysis. D7928-16
(2016b).
Azam, S. and Q. Li. “Tailings Dam Failures: A Review of the Last One Hundred Years.” Geotechnical
News (2010): 50-3.
Barrera, S., L. Valenzuela, and J. Campana. “Sand Tailings Dams: Design, Construction and Operation.”
Proceedings from Tailings and Mine Waste 2011, Vancouver, BC (2011): 1-13.
Barrera, S. and J. Caldwell. “Reassessment of best available tailings management practices.”
Proceedings from Tailings and Mine Waste 2015, Vancouver, BC (2015): 1-18.
Belem, T. and M. Benzaazoua. “An overview on the use of paste backfill technology as a ground support
method in cut-and-fill mines.” International Symposium on Ground Support in Mining and
Underground Construction, Perth (2004): 637-650.
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ABSTRACT
The MineSENTRY (Mine Safety and Rescue through Sensing Networks and
Robotics Technology) research project has developed a robotic system designed to
extend the range of wireless communications in mining environments. The robotic
system was designed to assist disaster response teams by allowing the team to control
teleoperated robots while remaining safely outside. The proof-of-concept system con
sists of a teleoperated vehicle and an Autonomous Mobile Radio (AMR). The AMR is
tasked with maintaining equal radio signal strength between the teleoperated vehicle
and the operator.
System testing was conducted at the Colorado School of Mines’ Edgar mine. Sev
eral simple controllers were developed to guide the AMR during these tests with
mixed results. Although the guidance controllers did not always perform well, the
results and data gathered proved useful for further development. Despite these per
formance difficulties, the results confirmed the capability of the MineSENTRY design
to maintain communication within a mining environment.
To address problems encountered with the tested guidance controllers, a unique
path-tracking controller is proposed which takes advantage of the inherent structure of
mining environments. The proposed controller departs from contemporary methods
by combining the environment and path into a single description. Further devel
opment is necessary before the controller can be implemented on the AMR robot;
however, the controller has been successfully tested in simulation with encouraging
results. It was found that the controller may alleviate the need for accurate and
expensive localization typical of current navigation methods.
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CHAPTER 1
INTRODUCTION
Robotics have gradually dispersed from the factory floor to commercial, private,
and military applications. Within the last few years there has been a growing interest
in applications for humanitarian missions such as search and rescue (SAR). Augment
ing SAR missions with robotics can potentially increase response time, efficiency, and
safety. Rescue operations benefit from mobility and advanced sensor technologies
allowing robots to reach or perceive environments that human rescuers cannot.
1.1 Motivation
Due to the harsh environments in which SAR robots are intended to operate,
development of SAR robotic systems has proven difficult and a number of key prob
lems have yet to be solved. Reliable communication between robots and operators is
one of these key problems. Mining environments are particularly difficult as neither
tethered nor wireless systems guarantee reliable communication even during normal
mining conditions. Wire tethers can experience a number of physical conditions
which result in either immobilization or communication loss. High frequency, high
bandwidth radio signals suffer greatly from propagation losses, resulting in severely
reduced range. Therefore, improving communication is a necessary component to
successfully utilize robots in mine rescue operations.
Improved communications would allow SAR robots to travel farther into mines
while providing invaluable sensor data to the rescue workers outside. In particular,
air quality must be known before rescue workers are permitted to enter the mine.
If the mine’s air quality measuring systems fail during a disaster, borehole drilling
is used to lower sensors from the surface into the mine—a time consuming process.
Robots could be deployed quickly and provide extensive air quality information and
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Figure 1.1: MineSENTRY Project Concept [1]
allow rescue workers to make better informed decisions. Furthermore, robots could be
teleoperated from outside the mine and used to search for victims long before human
operators could safely enter.
1.2 MineSENTRY Project
The MineSENTRY (Mine Safety and Rescue through Sensing Networks and Robotics
Technology) project was aimed at developing and demonstrating a multi-agent robotic
platform for mine search and rescue operations as a solution to the inherent commu
nication problems. The research project was carried out at the Colorado School of
Mines (CSM) and was funded by NIOSH Grant #1R010H009612-01 through CSM’s
Center for Automation, Robotics, and Distributed Intelligence (CARDI). The intent
was to create an ad hoc wireless network utilizing multiple Autonomous Mobile Ra
dios (AMRs) which can dynamically adjust to maintain communication between the
operator(s) and the lead teleoperated vehicle. To demonstrate the viability of this
system, a robotic system was developed at CSM and tested at Edgar mine in Idaho
Springs, Colorado—CSM’s experimental mine.
Conceptually, the MineSENTRY robotic platform maintains communication be
tween a teleoperated leader vehicle, a modified Bobcat front-end loader, and the
operator (Figure 1.1). The operator remains near the mine entrance while operat
ing the front-end loader located deeper within the mine, where it would otherwise
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be outside the communication range of a direct wireless link. Several AMRs follow
the front-end loader into the mine and position themselves to create a wireless mesh
network. The AMRs act as nodes within the network, each relaying information to
the next node until the information reaches its destination. The AMRs attempt to
maintain the highest radio signal strength (RSS) possible by automatically readjust
ing their positions. By using autonomous, mobile relays, the MineSENTRY platform
retains the freedom of wireless systems while drastically extending the robots reach
into the mining environment.
1.3 Thesis Objectives
To create the MineSENTRY robotic system, the MineSENTRY team had to de
sign, obtain, or modify the teleoperated leader vehicle, AMR, and radio systems. In
particular, the AMR was developed with the joint effort of several MineSENTRY team
members. The author’s primary responsibilities involved the design and construction
of the AMR, including actuation, hardware, power distribution systems, and controls,
although the author assisted in other aspects as well. Originally, software develop
ment was undertaken by other team members; however, due to their departure prior
to project completion, the author assumed responsibility for completing the control
software.
The objective of this thesis is to detail the development of the Autonomous Mobile
Radio, with a focus on the actuation, power, software, and control systems. Specifi
cally, the guidance controllers used during proof-of-concept testing are described along
with observations regarding their performance. Motivated by these observations, a
unique control approach is proposed for use with the AMR system; also, simulation
results are presented and analyzed. Documentation is provided to allow others to
continue, improve, or expand on this research.
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1.4 Chapter Overviews
To provide the aforementioned information, the thesis will cover each topic in the
following chapters:
Chapter 2 - Literature Review
In this chapter, a brief description is given regarding the use of mobile robots in
mining environments and SAR applications. Furthermore, an overview of the current
state of mobile robot navigation research provides the reader basic background infor
mation needed to understand later topics.
Chapter 3 - Development
Details of the AMR development are provided in this chapter. The topics covered
are AMR actuation, hardware, electrical, and software development. This chapter
is not intended to be a complete description of the AMR systems; rather, it com
plements the theses of other MineSENTRY team members, [1, 2], to provide the
documentation necessary to allow others to reconstruct the MineSENTRY systems.
Chapter 4 - Proof-of-Concept Testing
The results of several MineSENTRY tests, conducted at the Edgar mine, are pre
sented in this chapter. The results are focused on the guidance controller(s) used
during the tests to guide the AMR through the Edgar Mine. While many difficulties
were encountered that adversely affected performance, several successful tests were
conducted resulting in useful experimental data. This data motivated the develop
ment of an alternate guidance control, described in Chapter 5.
Chapter 5 - Controller
The unique guidance controller proposed by the author for use with the Mine-
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CHAPTER 2
LITERATURE REVIEW
The MineSENTRY project seeks to develop a robotic system for use in mine
search and rescue (SAR) operations. To gain a better understanding of the difficulties
involved and the reasons for undertaking such a project, a familiarity with the current,
germane research is necessary. Specifically, the current research on mobile robots,
their application in mining environments, and their application in search and rescue
operations must be evaluated.
2.1 Rescue Robots and Mines
In recent years there has been a great deal of interest in using mobile robots in
search and rescue (SAR) applications [3, 4]. The advantages of mobile robots and
SAR applications are diverse. For example, robots can provide communication and
sensor information while traversing terrain and environments which prove hazardous
for human rescue workers. Mining environments are an excellent example of one such
environment. In the event of a disaster, mines can quickly become inhospitable and
dangerous for both the miners and search and rescue teams. Mine SAR operations
would therefore benefit greatly from the capabilities of mobile robotic tools; however,
mines themselves present a number of challenges for mobile robots.
On January 2, 2006 an explosion occurred at the Wolf Run Mining Company’s
Sago Mine located near Sago, West Virginia, hereafter referred to as the Sago mine
disaster [5]. Approximately 20 hours after the explosion, the Mine Safety and Health
Administration’s rescue robot arrived from a repair facility in Knoxville, Tennessee
and was deployed in conjunction with a rescue team just a few hours later. The
robot, nicknamed V2 (Figure 2.1), was sent in to measure carbon monoxide levels
which would allow the rescue team to advance more quickly into areas where CO
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Figure 2.1: MSHA’s mine rescue robot, a Remotec ANDROS Wolverine robot [6].
levels were safe. Unfortunately, after a few hours of use, an operator error resulted in
damage to the robot’s wheels, disabling it for the remainder of the rescue operations.
The Sago mine disaster is an example of how robots may be used during actual
SAR operations. However, this disaster also reveals the challenges involved with using
robots in such an inhospitable environment. Some of these challenges include mobility
in rough terrain, control, power requirements, and communication [7]. MSHA’s rescue
robot utilizes a 5000 foot fiber-optic cable for communication which is susceptible to
damage and limits the robot’s range. Other researchers exploring robotic solutions
for mining environments have encountered similar difficulties with communication.
Abandoned mines are another safety concern for which robotic solutions are being
researched. In 2003, a robot nicknamed ‘Groundhog’ (Figure 2.2) was used to explore
and map the main corridor of an abandoned mine in Mathies, Pennsylvania [8]. The
robot was designed to operate autonomously outside of wireless communication, but
experienced problems during testing. Intervention over the wireless link proved un
successful due to the weak signal strength. Similarly, Pilania and Chakravarty are
focusing on communication in the development of their mine SAR robot [9].
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Figure 2.2: Groundhog, the abandoned mine mapping robot [10].
The goal of the MineSENTRY project was to develop a solution to the communi
cation problem encountered in mining environments. The solution is a wireless mesh
network comprised of autonomous robots that dynamically adjust to maintain com
munication. Each node (robot) within the network relays received signals to adjacent
nodes. In the MineSENTRY system, the operator transmits to the nearest AMR,
and the signal is relayed to the teleoperated vehicle. This simultaneously solves both
communication range and reliability issues, provided enough AMRs are available to
create a solid mesh network. However, with or without communication all robots (i.e.
the AMRs) are expected to have a certain degree of autonomy and operate indepen
dent from human control. Thus there is a need for autonomous navigation, which has
its own set of problems that must be addressed for the MineSENTRY project to be
successful.
2.2 Navigation
The literature on mobile robot navigation is extensive and diverse. Although
the research is extensive, no universal solution exists because each situation often
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poses its own unique challenges. In general, navigation methods can be divided
into landmark, beacon, dead reckoning, and map-based methods [11]. Map-based
methods are of particular relevance to the MineSENTRY project and are frequently
researched. To implement a map-based navigation system three challenges must be
addressed: mapping, localization, and path planning [12]. Additionally, the process
of following the planned path, referred to as path tracking, is a commonly researched
topic.
2.2.1 Mapping and Localization
Mapping is the process of exploring an environment and using sensor data to
produce a map which is useful (e.g. for localization) [13]. The guidance controller
proposed in Chapter 5 is designed to utilize survey maps that are commonly associated
with active mines. Because a survey map for the mine being used in testing is readily
available, no specific mapping techniques are implemented and thus no techniques will
be discussed. However, for the survey map to be useful, the map must be described
in a unique fashion which will be discussed in Chapter 5.
Localization is the process of determining the robot’s position and orientation
(collectively known as pose) from map and sensor data [13]. When implementing
or developing a localization algorithm, the absence of information known before and
during run-time greatly increases the complexity—and therefore difficulty—of the
localization process. For example, global localization is the process of localizing with
no initial information; in other words, the robot begins lost and must use the map to
determine its location. Additional complexity is encountered in the ‘kidnapped robot
problem’, which is global localization that further requires the robot to recognize if
it has been picked up and moved. Conversely, if the robot only needs to track its
deviation from a known initial position, the localization process is known as path
tracking [14]. Path-tracking localization is particularly important to the guidance
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controller proposed in Chapter 5, which will be elucidated in Section 2.2.3.
Originally mapping and localization processes were executed sequentially; how
ever, recent research focuses on simultaneous localization and mapping (SLAM) al
gorithms that perform mapping and localization processes simultaneously. The work
of Antoni Burguera is of particular relevance to the AMR. His extensive research ex
tends SLAM algorithms (which typically require high accuracy and data rich scanning
laser sensors) to the relatively inaccurate and sparse data associated with ultrasonic
range-finding (sonar) sensors—the sensor of choice for the AMR [15].
2.2.2 Path Planning
Path planning, also frequently referred to as motion planning, is the process of
finding a collision free path through 2-D or 3-D space. This process is roughly divisible
into global and local path planning. Global planning involves finding paths over long
distances to a goal location, typically with some sort of map; whereas local planning
involves finding the immediate motions the robot must make. Early research into
path planning generally focused on local methods, such as the potential field method
[16]. However, these methods had the disadvantage of being easily trapped in local
minima [17]. This disadvantage prompted research into other planning methods, such
as the Vector Field Histogram (VFH) [18] or Generalized Voroni Graph (GVG) [19]
methods.
Other approaches to path planning compute paths by finding the configuration
space (C-space) of the robot; then searching the C-space for acceptable solutions
to reach the goal state [11]. Configuration-based methods have the disadvantage of
being computationally intensive, but advances in computer technology have reduced
the cost of such methods. For example, the Groundhog robot applied the A* search
algorithm in C-space to plan its paths [8]. An advantage to configuration space
methods is its applicability to both global and local path planning.
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2.2.3 Path-Tracking Control
Once a collision free path is found via any of the numerous path-planning methods,
a path-tracking controller is required to guide the robot along the desired path. Path-
tracking control is a very diverse research topic. Path-tracking controllers use a
large variety of techniques including fuzzy logic [20], neural networks [21], spatial
methods [22], and many others. Often vehicle controllers focus on incorporating the
kinematics (or dynamics) of the vehicle being controlled [23]. However, if accurate
localization data is available, and the operating conditions allow the vehicle dynamics
to be ignored, simple path-tracking controllers may be implemented. For instance,
the Groundhog robot [8] utilized a simple Proportional-Derivative (PD) controller to
track and follow the paths generated by its path planner.
The guidance controller proposed in Chapter 5 is categorized as a path-tracking
controller. However, the proposed controller adopts a unique approach to the path-
tracking problem in an effort to reduce the need for accurate localization. To ac
complish this, the algorithm requires a unique way of describing the path. This path
description is a key contribution of the thesis.
2.3 Summary
There are many advantages to using mobile robots in mining environments; how
ever, there are also technical challenges that impede their implementation. MSHA’s
mine rescue robot, V2, and the abandoned mine mapping robot, Groundhog, are ex
amples that exemplify both the advantages and challenges. Recognizing these chal
lenges, the MineSENTRY project seeks to address the problem of communication
within mining environments. To accomplish this goal, the MineSENTRY research
team sought to develop the Autonomous Mobile Relay (AMR) robot; which, as its
namesake implies, requires a certain degree of autonomy. Current research into mo
bile robot navigation reveals many options that could have been used to implement
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CHAPTER 3
DEVELOPMENT
Before discussing the control algorithms implemented on the AMR, it is important
to understand the platform upon which it was developed. As previously mentioned,
the MineSENTRY research team built the AMR robot using a golf cart as the vehicle
platform. In order to convert a golf cart into a robot, a number of systems had to
be developed including actuation, hardware, electrical, and software. Each of these
topics are discussed separately in the remainder of this chapter.
3.1 Actuation
To transform a golf cart into the AMR robot, the control electronics must be able
to control the steering, brake, and throttle. Thus, actuation was installed that allows
electronic commands to be realized as mechanical motion. The steering and brake,
both purely mechanical on the stock golf cart, required the addition of electrome
chanical actuators; however, the golf cart’s throttle was electromechanical initially,
and only required additional circuitry to allow computer control. Before adding any
actuation, several requirements were established to govern the design process. These
requirements will be discussed in the following sections; however, a universal require
ment was to keep modification to the factory golf cart minimal. By reducing the
amount of modification necessary, the design becomes nearly modular. Provided the
necessary components as a kit, a small team of technicians could easily mount an
identical system onto another golf cart with minimal time and effort.
3.1.1 Steering Overview
The requirements for the steering actuation included the necessity to return the
vehicle to fully manual steering. This capability is extremely important both during
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software and hardware development. During testing, it occasionally became necessary
to manually drive the vehicle back to the lab to fix software problems that would have
otherwise rendered the vehicle immobile.
Allowing for manual steering requires the steering shaft to be mechanically de
tached from the actuator. The mechanical disconnect is necessary because back-
driving the servomotor’s gear box results in unneeded mechanical wear and could
damage the gears; furthermore, back-driving the servomotor adds unneeded resis
tance to the steering during manual operation. Damage to the servomotor gear box
became a primary concern because of the relatively low output and input torque limit
specifications. The steering can be easily back driven by applying force at the wheels,
creating concern that a solid impact at the wheel could cause a transient torque at
the servomotor in excess of the gear box limit. The DC servomotor was also capable
of exceeding the input as well as the output torque limit of the gear box.
Several solutions for protecting the gear box were considered including commer
cially available friction clutches, friction-based mechanisms, and shear pins. Most
commercial friction clutches had insufficient torque ratings given the design require
ments. Friction-based systems, such as a cone shaped friction clutch, could provide
both gear box protection and a means of disengaging the steering. However, to pro
vide the torques required the clutch would either need to be prohibitively large or
require high axial forces while engaged. Thus, the author decided to use a shear pin
for the gear box protection due to its simplicity and reliability. In order to disengage
the steering actuator, a positive locking assembly was designed to lock and unlock the
driving pulley from the steering shaft. A full description of the final steering assembly
as well as the actuator description and selection process is described below.
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Steering Column
Firewall
Figure 3.1: Modified Steering Column
3.1.2 Steering Assembly
Before going into the details of the steering assembly, it’s necessary to describe
the stock steering assembly of the golf cart. The stock golf cart steering assembly is
very simple; consisting of a hollow steering column, a steering shaft, steering wheel,
universal joint, and a rack and pinion drive. The steering column is a steel tube
that bolts to the firewall and houses the steering shaft. The steering shaft is directly
attached to the steering wheel and to a universal joint behind the firewall. Finally
the universal joint is connected to a rack and pinion steering assembly (Figure 3.1).
To minimize the modifications required to add actuated steering, the entire assem
bly is attached to the steering column near the steering wheel. The only modifications
needed to attach the steering assembly are to shorten the steering column and add a
Woodruff key seat to the steering shaft. The actuator assembly clamps to the end of
the steering column and replaces the stock bushing used to stabilize the end of the
steering shaft.
As shown in Figure 3.2, there are several components used in the steering assembly.
The servomotor which directly drives the drive pulley. The drive pulley is not directly
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Figure 3.2: Steering Actuation Assembly
fixed to the servomotor output shaft; rather, a steel sleeve is pinned to the servomotor
output shaft and the drive pulley can freely rotate around the sleeve. The end of
the sleeve is tapped and keyed. An end cap is notched to match the sleeve’s key
and is fastened to the sleeve with a cap screw. The end cap rotates the drive pulley
through a shear pin. The shear pin protects the steering actuator gearbox by breaking
if the torque on the output shaft exceeds a certain limit. The drive pulley drives
the steering pulley through a timing belt. The steering pulley is mounted on the
steering shaft but may freely rotate on the shaft. To drive the steering shaft, a
spline assembly is keyed to the steering shaft. The spline assembly sits next to the
drive pulley, and by virtue of the splines can rotate with the steering shaft and
have some axial motion. The axial motion of the spline assembly is used to fix the
steering shaft to the drive pulley via a steel pin. The geometry of the purchased
steering pulley required the addition of an inset brass disk to provide the mating
holes for the steel pin. By adjusting the spline assembly to engage or disengage the
steering pulley, the servomotor may be mechanically disconnected from the steering
column. This allows the golf cart to be driven manually without back-driving the
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Figure 3.3: Brake Actuator Assembly
requirement can be easily achieved.
3.1.5 Brake Assembly
The selected brake actuator is a common linear actuator with potentiometer po
sition feedback. The stroke length, force limit, and linear speed were selected based
on the previously mentioned specifications. To allow manual operation of the brake
at all times, the brake actuator is not rigidly connected to the brake; rather, a sleeve
is connected to the actuator piston and a push rod is mounted to the brake pedal
lever (Figure 3.3). This configuration allows the actuator to push on the brake lever
but retract independently of the brake. Note that if the brake actuator is left in
an extended position while the golf cart control systems are turned off, the brake
cannot be released manually. To resolve this, the golf cart control systems are set to
automatically release the brake when placed in manual mode.
The manual braking requirement created some small problems with the remote
or automatic control modes. The stock golf cart has no mechanism to prevent the
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Figure 3.4: Unmodified Golf Cart Throttle Sensor
allows manual operation at any time.
Unfortunately mimicking the sensor was not an easy task. The sensor itself es
sentially acts as a variable resistor which changes its resistance based on the position
of the plunger. The difficulty arises from the fact that the sensor is not grounded
but rather floats at some voltage above ground. Without access to the stock golf
cart controller’s circuitry, or a schematic thereof, this behavior could not be reliably
reproduced. Consequently to mimic the sensor, the throttle control circuit had to be
electrically isolated from ground through an isolating transformer. Figure 3.5 shows
the throttle control circuit, which is mounted underneath the seat near the golf cart
controller. For a more detailed description of the throttle control circuit, please refer
to [2].
3.2 Hardware
This section discusses the hardware required to mount or enclose the electrical
components of the AMR, particularly sensors. In total, 24 sensors are mounted to the
golf cart: fourteen ultrasonic range finders (sonars), four infrared range finders (IRs),
four Hall effect encoders, one string potentiometer, and one brake switch. Detailed
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Figure 3.8: Brake Switch
enclosure that houses the Vehicle Controller, power supplies and distribution boards
was carefully selected, modified, and mounted to the golf cart. The actuator control
board, which provides the high-power drive circuitry and low-level feedback control
for both actuators, was mounted in the front of the vehicle in a pre-existing storage
compartment.
The primary enclosure is a Bud Industries steel NEMA enclosure and was orig
inally selected to contain the Vehicle Controller (microcontroller board), actuator
power supply, power distribution unit, and the Autonomous Controller (single board
computer). Although the space was allocated, the single board computer was aban
doned in favor of a netbook as the Autonomous Controller due to software issues. The
enclosure is mounted using the golf bag rack on the golf cart (Figure 3.9). The bag
rack has many pre-existing holes which may be tapped using a 1/4-20 tap. Simple
mounting brackets were built to attach the primary enclosure and vibration dampers
were used to reduce the shock loadings felt by the components mounted inside. A
hole was cut on the bottom of the enclosure to allow cable routing and has an inset
nylon brush grommet to help protect the enclosure from dust and debris. A hole was
cut in the back of the enclosure to mount a cooling fan. The cooling requirements
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(a) Roboteq AX3500 (b) Enclosure
(c) Breakout Board
Figure 3.10: Actuator Controller Board
ditional control electronics include the Raj ant radio (see [1] for details), ethernet
router, and ethernet camera which are needed for the MineSENTRY mission goal
but are not required to operate the vehicle in manual, remote, or automatic modes.
The following provides a brief description of the electronics systems, further detail is
provided in the appendices where noted.
3.3.1 Power Distribution
The power distribution is particularly important because the vehicle is intended
to operate wirelessly and thus must carry its own power supply. Fortunately the
stock golf cart is powered by six deep cycle batteries connected in series providing
a total voltage of 36 V. However, due to the excessive noise created by the golf cart
drive motor these batteries were not used to power the more sensitive electronics.
The control electronics are powered by an additional 12 V deep cycle battery which
provides several hours of operation without recharging (Figure 3.11). These two power
sources are used to supply all the power needed for operation using the following power
distribution scheme shown in Figure 3.12:
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The control electronics are organized such that lower-level functionality (e.g. com
municating with sensors) is handled by the Vehicle Controller which is a digital signal
processing microcontroller from Microchip. The Vehicle Controller reads the sensor
outputs and converts the raw electrical signals to integer values with appropriate
units. For example, the sonars output an analog signal that the microcontroller con
verts to distance measurement in millimeters. The Vehicle Controller also handles
communication with the actuator control board which drives the actuators.
The actuator control board is a Roboteq AX3500 driver board that is capable
of driving/controlling two actuators (Figure 3.10). The AX3500 implements a PID
control loop and can accept a variety of position feedback signals. Both the brake
actuator and the steering servomotor are controlled by this board. The servomotor
uses an optical encoder for position feedback and the brake actuator uses an analog
potentiometer for position feedback. The position setpoints are sent by the Au
tonomous Controller to the actuator control board via an RS-232 connection to the
Vehicle Controller. Other than some low-level control functions, such as releasing the
brake or calibrating steering, the Vehicle Controller simply relays brake and steering
commands from the Autonomous Controller to the actuator controller.
The Autonomous Controller is a netbook laptop running control software. The
Autonomous Controller handles higher-level functions such as interpreting sensor sig
nals and guiding the robot through the environment. Additionally, the Autonomous
Controller communicates to the other AMRs and the base station through the Raj ant
radio mesh network. The Autonomous Controller can receive and interpret commands
from the base station that determine how the AMR should move to maintain radio
signal strength. These operations are implemented using the high-level programming
language Python.
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3.4 Software
Operation of the AMR requires a variety of software. There is software imple
mented on the base station which oversees and controls the mission objectives. The
Autonomous Controller software communicates with the base station and determines
the specific AMR control actions. Finally, there is firmware implemented on the Ve
hicle Controller which handles all the lower-level control functions of the AMR. The
author did not contribute to the development of either the base station software or
the microcontroller firmware, so neither will be discussed. The details of the base sta
tion software and microcontroller firmware may be found in [1] and [2], respectively.
Initial development of the Autonomous Controller software was performed by Chris-
ter Karlsson and Ken Anderson, but was completed by the author with contributions
from Chris Meehan.
The autonomous control software is implemented in the high-level programming
language Python and may be run from a Linux or Windows computer. The user inter
acts with the software through a custom graphical user interface (GUI), a screenshot
of which is shown in Figure 3.13. Under Global Settings, the user must first select
the communication port and perform a communication test to initiate a link with
the Vehicle Controller. Currently, the communication is accomplished with a USB
to serial adapter cable. If the Vehicle Controller is on and properly connected to
the computer (Autonomous Controller), the communication test will connect to the
Vehicle Controller and start passing data. Once the communication test is performed
successfully the other GUI functionality becomes available.
Most of the GUI’s screen real estate is used for monitoring sensor feedback and
displaying status information. On the bottom edge of the window, a series of indica
tors display the AMR control mode, drive direction, and brake status. In the center
of the window there are two graphing areas which can each plot data for one sensor.
The graph source can be selected via the drop-down boxes under Global Settings,
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The Raj ant server is a separate program that is written in Jython. Jython is a
Java-based implementation of Python and was required to utilize the Raj ant radio
API which is written in Java. The Raj ant server program was coded by Chris Meehan
to allow communication between the AMR and the base station over the Raj ant radio
mesh network. The Raj ant server receives data from the mesh network, relays it to
the data logger and sends mission-based commands to the control thread. Note
that there is no queue feeding the Raj ant server because it uses a different internal
communication method.
Besides queues, flags are used to communicate the status of the threads. The
flags are Boolean values and are primarily used to assist thread communication. For
example, when the RC-Comm thread receives new data it checks the flags of the other
threads to see if they are ready to receive more data. If the flag is cleared, RC-Comm
puts data into the corresponding queue and sets the flag. When the receiving thread
retrieves the data from the queue, it clears the flag to indicate it is ready for more
data. In the case of the Raj ant server, the control thread uses flags to indicate when
it has completed the most recent command and is awaiting more commands.
3.5 Summary
To summarize, the actuator assemblies allow manual, remote, and automatic con
trol operation of the vehicle. The Vehicle Controller handles all low-level tasks such as
gathering sensor data, monitoring E-stops, and relaying information to/from the Au
tonomous Controller. The Autonomous Controller handles higher-level tasks such as
communicating with the mesh network, guidance control, and user interface. Finally,
the software defines the operation of both the Vehicle Controller and Autonomous
Controller. With a basic understanding of the hardware, electronics, and software
implemented on the AMR, we may now begin to discuss the guidance controller de
veloped on the AMR platform.
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CHAPTER 4
PROOF-OF-CONCEPT TESTING
The MineSENTRY team performed proof-of-concept testing at the Edgar mine
in Idaho Springs, CO. For these tests, three simple guidance controllers were de
signed and implemented on the AMR’s Autonomous Controller with the intent of
autonomously navigating a portion of the Edgar mine’s Army tunnel. While a fully
functional guidance controller would be expected to control the speed, brake, and
steering of the vehicle, these test controllers focused on controlling the AMR steering
only. During the testing described in this chapter, the speed was set to a constant
value (maintained by the Vehicle Controller); the MineSENTRY base station auto
matically commanded the AMR when and how far to move. The test controllers
were:
• A fuzzy logic controller designed to recognize key situations and react appro
priately.
• Two basic wall-following controllers:
— a center-drift wall-following controller designed to ignore side-passages
(designated as Controller 1).
— a wall-following controller designed to switch between left, right, and center
following (designated as Controller 2).
Each guidance controller was tested within the hallways of CSM’s engineering
building, Brown Hall, as well as the Edgar mine in Idaho Springs, CO. Detailed
descriptions of the proof-of-concept testing, specifically regarding the radio signal
strength results, may be found in [1]. The development, testing, and performance of
the guidance controllers is detailed in the following text.
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M
n
Figure 4.1: Brown Hall Test Path
hallways. Because the infrared sensors are mounted in different locations this also
required that the controller be trained with a new set of situations specifically for the
IR range finders. Unfortunately, sensor data is unavailable for these tests because the
Python control software was incomplete at this time. Despite the issues encountered,
some successful results with this controller were obtained.
While testing in the Brown building, the desired path for the robot was a straight
line past a T-junction (Figure 4.1). After a few dry runs to tune the controller
and adjust the threshold values for detecting passages, the controller successfully
guided the robot past the T-junction to the opposite end of the hallway several times.
However, it was noted that the performance around the T-junction was not completely
reliable. Conversely, the performance in the straight sections of the hallway was
excellent. Several test runs were conducted with the robot set initially to the right
or left of the hallway or turned at angles from straight. In each instance, the robot
successfully managed to navigate to the center of the hallway and straighten out.
A significant amount of the controller tuning and adjustment was focused on
obtaining the desired response at the T-junction. If the controller did not correctly
identify the T-junction as a passage on the left, the robot would begin to turn left
then drive into the opposite corner. Even if the passage was detected, the robot would
tend to veer to the left side and over-correct at the reappearance of the wall on the
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Lj
t — 1— 1 1
— :
Figure 4.2: Typical failures encountered while tuning fuzzy controller.
left, or in some cases not correct at all (Figure 4.2).
Once the controller was properly tuned, the performance near the T-junction
became more reliable as long as the robot was nearly centered and driving straight.
The tuned controller made several successful test runs, driving both East to West
and vice versa, where the T-junction had almost no noticeable effect on the path of
the robot. At this point, we determined that the fuzzy logic controller was ready for
testing at the mine.
4.1.3 Experimental Results - Edgar mine
On March 11, 2010 the AMR was transported to Edgar mine to perform the proof-
of-concept testing previously described. The fuzzy logic controller’s performance was
mixed however and passage detection performed poorly. Even in the straight sections
of the mine the controller would tend to veer to one side and the robot had to be
stopped before impact. After debugging it was discovered that a few sonar sensors,
including two of the 45° sonars important for passage detection, had been damaged in
transport and were reporting fixed data readings (Figure 4.3). After discovering the
damage to the sensors, the fuzzy-logic guidance controller was disabled. The proof-
of-concept testing, of the base station and radio systems, continued with a human
operator to steer; while the Autonomous Controller regulated the AMR’s speed as
commanded by the base station.
The MineSENTRY team returned to Edgar mine on April 2, 2010 for further test-
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Sonar 2 Sonar 3
i i
1 1 ' 1 1 1 1-----------1-------------1-------------
w r m w r ^
i;
j i i i i i i i___
0 20 40 60 80 100120140160180 0 20 40 60 80 100120140160180
Time (sec) Time (sec)
Sonar 5 Sonar 6
i i
i i ( r
0 20 40 60 80 100120140160180 0 20 40 60 80 100120140160180
Time (sec) Time (sec)
Figure 4.3: Plots showing sonars 3, 5, and 6 which were damaged during transport.
Sonar 2 was working and is shown for comparison. The AMR was moving between
20-45 seconds and was stopped otherwise. The spikes seen in sonar 3’s data were due
to communication errors that were resolved in later versions of the code.
ing of the radio system. The failed sensors had been replaced and extra precautions
were taken during transport to protect the AMR. Unfortunately, even with properly
functioning sensors the controller was unable to navigate past side passages. Thus,
the tests took place in a straight section of the Army tunnel as indicated in Figure 4.4.
The controller performance in the straight section of the mine mirrored the per
formance observed in Brown Hall. The AMR successfully followed the leader (a
MineSENTRY team member with a Raj ant radio) using radio signal strength (RSS)
information relayed by the base station. The AMR repositioned five times during
the test. While the AMR was stopped, the leader would reposition and the RSS
signal strength information logged. The approximate path taken by the AMR as well
as the sensor data are shown in Figure 4.5. The sensor data is split between the
45° sensors and the side facing sensors to compare the performance. Interestingly,
the performance of the 45° sensors appear to be better than the side facing sensors
in these tests, but the plot for the side facing sensors shows three sensors on either
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Miami Tunnel
Army Tunnel
Figure 4.4: Testing area during the April 2, 2010 tests. The AMR traveled from right
to left and stopped at the turn.
side. The variation observed in the side facing sensors is due to calibration inaccu
racy and signal filtering, resulting in slightly different reported distances on the three
side facing sensors. In later experiments the sonars were re-calibrated and electronic
filtering added which improved the reliability of the sensors. Table 4.1 summarizes
the observed performance of the fuzzy logic controller.
The issues with the sonar sensors create difficulty assessing the performance of
the fuzzy logic controller. The controller may perform better if tested with properly
calibrated, functioning sensors. However, it was decided the results of the April 2
testing was clear enough to pursue other controller solutions.
Table 4.1: Guidance Controllers Performance Summary
Controller Summary
Location Controller Performance Summary
Brown Hall Fuzzy Logic Successfully navigated T-junction, but is sensitive to
tuning parameters. Excellent performance in the hall
way.
Edgar Mine Testing on Mar. 11, 2010, controller failed due to
Fuzzy Logic
broken sonar sensors.
Testing on Apr. 2, 2010, controller could not navigate
side passages, but performed well in straight sections.
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Robot Position & 45 ° Sensor Readings
x-position
Robot Position and Side Facing Sensor Readings
-2
^ -
-4 - ^
-60 -50 -40 -30 -20 -10 0 10
x-position
Figure 4.5: AMR’s approximate path and sensor readings from the April 2, 2010
tests. As in Figure 4.4, the AMR traveled from right to left in the straight section of
the tunnel.
4.2 Wall-Following Controllers
On November 9, 2010 the MineSENTRY team returned to the Edgar mine to
conduct additional proof-of-concept testing. Unlike the previous testing, during which
a team member simulated the leader vehicle, the converted Bobcat front-end loader
was teleoperated by an operator sitting at the base station. During this test people
were left out of the system loop except for the Bobcat operator. The desired path
through the Army tunnel remained the same as the previous testing, where the path
followed the Army tunnel while ignoring any side passages. Unlike the previous
testing, two controllers were designed and used during testing.
4.2.1 Controller Descriptions
Two wall-following controllers, as mentioned on page 39, were designed for this
proof-of-concept testing. They were designed and tested in tandem and are referred
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fixed number of the previous sensor values, the buffer is updated based on the odome
ter data. The buffered values represent average sensor readings taken in increments
of 0.2 meters, and a history is maintained for the last four meters. The buffer also
stores values indicating if the robot was left, right, or center following at the time
the value was buffered. When a passage is encountered, the buffer is searched to
determine what the setpoint distances were while the robot was still center following.
Furthermore, the distances are computed differently from Controller 1. Controller
2 uses the six sensors which point directly to each side of the robot. The distance to
the left or right wall is computed by taking a separate weighted average of the sensors
for each side. The weighting is determined by the magnitude of the sensor values;
the smaller sensor readings are weighted heavier than the larger sensor readings.
Originally, the sensor values were averaged, but this was modified during testing in
Brown Hall. The weighting was implemented to ensure that sensor readings indicating
close proximity to the wall were not ignored when averaged with a larger reading (e.g.
when the robot is angled toward the wall).
4.2.2 Experimental Results - Brown Building
As with the fuzzy logic controller, both of these controllers were tested in the first-
floor hallway of Brown Hall. The desired path and desired response were also the
same: drive past the T-junction while maintaining the straightest path possible. Both
controllers were tuned separately to achieve this goal and both performed slightly
differently within the hallway. Because these tests were used to design and tune the
controllers in preparation for testing at the Edgar mine, no sensor data was logged
for later analysis. However, Figure 4.6 shows the difference between the 45° and side-
facing sensors utilized by Controller 1 and Controller 2, respectively. The sensor data
was logged at a later date while driving the AMR through the hallway manually.
Controller 1 tended to veer towards the left when passing the T-junction. After
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45° Sensor Readings
Side Facing Sensor Readings
5-40-35-30-25 —20—1 1 0 —5 5-40-35-30-25-20-15-10 —5 0
x - position (m) x-position (m)
Figure 4.6: Comparison of the 45° and side-facing sonar sensors’ readings while
driving the AMR in Brown Hall.
passing the T-junction, it often settled into a low amplitude steady-state oscillation
while following the center of the hallway. The simple passage detection performed
very well, but due to the aforementioned problems with the 45° sonars detecting the
smooth walls the control was jittery (i.e. the steering jerked frequently).
Conversely, Controller 2 tended to follow the right wall more closely when the wall
on the left disappeared at the T-junction. After passing the T-junction, it tended
to over-correct but settled into following the center of the hallway steadily. Due to
the averaging of multiple sonars, as well as the more consistent performance of the
side-facing sonars, the control response was smoother than Controller 1.
4.2.3 Experimental Results - Edgar mine
Both of these controllers were tested on November 9, 2010 before the the main
proof-of-concept testing with the intention of using the better performer in the fully
autonomous tests. The proportional control of both wall-following controllers needed
to be tuned for the mine. In each case, the proportional gain had to be reduced.
However, the performance characteristics of these controllers seemed to switch in the
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A Base Station
Cross Cut
Army Tunnel
Y in Tunnel
(a) Edgar mine Map
Approximate Robot Path and Sensor Readings Approximate Robot Path and Sensor Readings
10 1U
8 8
6 6
4 4
2 2
0 0
-2 -2
-4
0 -4 0
-20 -15 -10 -15 -10 -5
x-location (m) x-location (m)
(b) Controller 1 (c) Controller 2
Figure 4.7: Plots showing approximate AMR path and sensor readings while navi
gating passed the cross-cut. Only the sensor readings from the six side facing sensors
are shown for clarity.
mine. Controller 1, which showed steady-state oscillation in the Brown Hall hallway
showed little to no oscillation in the mine tunnels. On the other hand, Controller
2 showed a fair amount of steady-state oscillation in the straighter portions of the
mine tunnels. The performance of the controllers at the Y just after the cross-cut,
the locations of which are shown in Figure 4.7(a), changed.
The cross-cut was believed to be the most difficult location for the controllers
to navigate. However, both controllers managed to successfully navigate past this
location in testing (Figure 4.7). The Y further down actually proved to be more
difficult for both controllers. Controller 1 never successfully passed the Y in the
mine. Upon entering the widened area, the trajectory of the robot carried too far
to the right where it encountered the corner of the Y. Controller 2 also tended to
do the same thing, but after a number of adjustments managed to bear left enough
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Approximate Robot Path and Sensor Readings
20
10
I
-20
—80 —70 —60 —50 -40 —30 —20 —10 0
x-location (m)
Figure 4.8: AMR navigating using Controller 1 in the tunnel after the Y (moving
from right to left). Only the sensor readings from the six side-facing sensors are
shown.
to pick up on the right wall again and continued through the tunnel. Unfortunately
after passing the cross-cut, Controller 2 would often begin oscillating at some distance
down the tunnel and would have to be stopped before hitting the wall (Figure 4.8).
It is important to note that during the proof-of-concept tests the base station
would command the AMR to stop around the Y. This could have been detrimental to
Controller 1, which used buffered sensor values to account for side passages. Because
the buffering was only based on time, the buffer would fill with readings from the same
location. This problem should not have affected Controller 2 which buffered previous
sensor values based on the distance traveled as opposed to time. Controller 2 had
one successful navigation past both the cross-cut and the Y as shown in Figure 4.9.
Unfortunately the radio signal strength information received from the base station
was incorrectly calibrated for this test run. This caused the AMR to move in small
increments of approximately 10 ft; thus, this particular test run was stopped short so
adjustments could be made at the base station.
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Approximate Robot Path and Sensor Readings
25
20
15
t
10
! •
o
-5
-10
-40 -30
x-location (m)
Figure 4.9: AMR successfully navigating past the cross-cut and the Y in the Army
Tunnel.
Despite the difficulties encountered with these controllers, a number of successful
proof-of-concept tests were completed. Due to the difficulty of resetting the Bobcat
leader for each test and the controller’s unreliable performance near the Y, the proof-
of-concept test was divided into two parts. In the first part, the AMR started near
the base station while the Bobcat was driven further down the Army tunnel. The
AMR was unmanned and autonomously adjusted to maintain radio signal strength
between the Bobcat and the base station. Once the AMR reached the Y, it was
manually repositioned past the Y and the experiment continued. If the controller had
proved capable of autonomously navigating past the Y consistently then no human
intervention would be necessary for the entire duration of the test. Table 4.2 provides
a summary of the performance for each of the tested guidance controllers.
4.3 Sonars in the Mine
The performance of the ultrasonic range-finding sensors (sonars) within a mining
environment is very important to consider while developing a controller for the AMR.
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Table 4.2: Tested Guidance Controllers Performance Summary
Controller Summary
Location Controller Performance Summary
Brown Hall Fuzzy Logic Successfully navigated T-junction, but is sensitive to
tuning parameters. Excellent performance in the hall
way.
Controller 1 Successfully navigated T-junction, but had steady
state oscillation in the hallway.
Controller 2 Successfully navigated T-junction, no steady state os
cillation in the hallway.
Edgar Mine Testing on Mar. 11, 2010, controller failed due to
Fuzzy Logic
broken sonar sensors.
Testing on Apr. 2, 2010, controller could not navigate
side passages, but performed well in straight sections.
Controller 1 No issues with cross-cut, but could not navigate
passed Y afterwards. No steady state oscillation ob
served in straight tunnel sections.
Controller 2 No issues with cross-cut, successfully navigated
passed Y once. Steady state oscillations in straight
sections required human intervention to prevent
crashing.
The data gathered during the tests on April 2 and November 9 of 2010 reveal some
potential issues that should be addressed in future development of the MineSENTRY
robotic system.
Consider Figure 4.10 which shows the approximate path and sensor data for one
of the November 9th tests. The sonars’ results have been separated for clarity and
comparison. The plots for the 45° sonars indicate that, similarly to the results ob
tained in Brown Hall, the incident angle may cause a large variation in the reported
distance. This result was unexpected because the walls within Edgar mine are far
from smooth. The rock walls of the Edgar mine are ragged and broken and deviations
from average wall position easily exceed 1 ft (w 300 mm). However, these errors only
appear in the forward facing sensors (sonars 5 and 6) while the rear facing sensors
(sonars 11 and 12) are actually quite stable (refer to Figure 4.11). The distances re
ported by the front sonars, especially in the region —40 m < % < 0 m, report distance
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Sonar 5 Facing Left-Front Sonar 6 Facing Right-Front
y
i i
1 1 1 1 ( 1
!
.......i..................
0 50 100 150 200 0 50 100 150 2(
Time (see) Time (sec)
Sonar 11 Facing Left-Rear Sonar 12 Facing Right-Rear
.. ..........1.............!
! r
o
63
J jU iN w
0
0 50 100 150 200 50 100 150 200
Time (sec) Time (sec)
Figure 4.11: 45° angled sonar sensor plots for Nov. 9th Edgar mine test.
readings up to three meters above the average value. The systematic positive error
seems to indicate that the ultrasonic sound wave is not being reflected back to the
sensor reliably. However, because the angled sensor in the rear are reporting reliably,
electric noise on the sensor cables could also be the cause.
The results of the side-facing sensors are far more reliable and, with some excep
tions, perfectly acceptable considering the variation in the mine walls being sensed.
Please note that the readings on the far left side of the plot may have captured Mi
neSENTRY teams members at the end of the test. The readings for the right side of
the robot (top, blue) have very little noise and in most regions all three sonars (4,
8, and 14) fall within an approximatley 0.5 meter range or less. These results are
acceptable considering the natural variation of the mine wall is of the same order of
magnitude. As shown in Figure 4.12, the right facing sonars produce very consis
tent readings with occasional spikes on the order of 1-2 m. These spikes are most
likely caused by electrical noise and may be easily eliminated with software filtering.
Unfortunately, the dsPIC microcontroller only samples the sonar analog output once
per sonar update, thus increasing the susceptibility to electrical noise despite the low
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Sonar 4 Facing Right in Front Sonar 8 Facing Right in Rear
S S
I i
100 150 200 100 150 200
Time (sec) Time (sec)
Sonar 14 Facing Right in Middle
S
I
100 150 200
Time (sec)
Figure 4.12: Right side sonar sensor plots for Nov. 9th Edgar mine test.
pass RC filters on the signal lines.
The results for the left facing sonars (2, 10, and 13) are similar (Figure 4.13)
except for the large amount of noise observed on sonar 10. The tendency for sonar
10 to return lower values is most likely due to the sensor being pointed at a slight
downward angle. The MaxBotix LV-MaxSonar®-EZ3 sensors’ feature a beam pattern
that can detect objects within a % 40 cm radius of the sensors center line. Thus a
slight downward angle may allow the sensor to detect rocks on the tunnel floor or the
tunnel floor itself. Furthermore, the tunnel cross section is somewhat rounded and
in many locations the width of the tunnel is narrower near the floor. It is expected
that a simple mechanical adjustment could greatly improve the reliability of sonar
10’s readings. If this is the case, the variation in the left facing sonars are acceptable
as well.
The forward and rear facing sonars present some problems. For most of the testing
the forward facing sonars (sonars 1 and 3) read a full 6 meters, but the rear facing
sonars (7 and 9) were highly noisy (Figure 4.14). Although electrical noise may be
present, the primary cause for the noisy readings is due to the sensing pattern for the
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Sonar 2 Facing Left in Front Sonar 10 Facing Left in Rear
1 - 6 ............r.-.......'•™r......
I 4 :...........'...........1.......r ..r t . & 4 IM I rU
! 2
^ 0
50 100 150 2 K 50 100 150 20
Time (sec) Time (sec)
Sonar 13 Facing Left in Middle
50 100 150 200
Time (sec)
Figure 4.13: Left side sonar sensor plots for Nov. 9th Edgar mine test.
forward/rear facing sensors. The original design intent was to use the forward and
rear facing sensors for collision avoidance (i.e. to allow the vehicle to detect an object
in its path). To fulfill this requirement, sonars with a wider sensing pattern were
selected that could detect any object across the width of the AMR. Unfortunately,
the sensors selected have too broad a sensing pattern (% 1 m radius from sensor
center line) and often detect the floor or nearby walls. Tests within the hallways of
Brown Hall indicated the front/rear facing sensors could detect door frames or even
fire alarms on the walls. It would be beneficial to replace these sonars with the same
model used for the side-facing sonars and, if needed, use one broad range sonar on
the front and back of the vehicle for collision detection.
4.4 Summary
To fully test the concepts set forth by the MineSENTRY research project, guidance
controllers for the AMR were designed and implemented. Although the controllers
operated as intended within building hallways, the controllers performed inconsis
tently within Edgar mine’s rough, unordered tunnels. To a degree, each of the three
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CHAPTER 5
PROPOSED CONTROLLER
The unreliable performance of the guidance controllers presented in Chapter 4
proved that a simple guidance controller is not sufficient for successful AMR nav
igation. A more reliable controller able to navigate the more complex areas of the
mine is necessary. However, the simple wall-following controllers’ successes within the
straight sections of the mine should not be ignored. The guidance controller proposed
in this chapter extends the wall-following approach of the Chapter 4 controllers and
provides a unique approach to the navigation discussed in Chapter 2.
5.1 Controller Approach
This chapter describes the development of a guidance (or path-tracking) controller
that does not require highly accurate map data. Since active mines are typically
documented with structurally detailed survey maps, a controller of this type would be
very useful for quick deployment in an emergency situation. The controller proposed
in this chapter attempts to address these requirements with a unique approach to the
path-tracking problem.
Recent research focused on mobile robot navigation typically relies on an abun
dance of sensor data to perform mapping and localization (SLAM). With accurate
map and localization data, the robot can plan a path through two or three-dimensional
space and follow that path using a path-tracking controller. In Probabilistic Robotics,
localization is described as the process of referencing the robot’s position with respect
to an external reference frame [14]. This is the primary difference between current
techniques and the path tracking controller presented in this chapter. To be more pre
cise, rather than referencing the map, path, and robot to a common reference frame,
the path and robot position are referenced directly to the environment in which it is
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Figure 5.1: Example of the variable resolution path sampling for a path around a
simple right turn.
the environment. The purpose of the cost function is to eliminate path samples that
are indistinguishable from their nearest neighbors. In regions where the features
are unchanging (e.g. a long tunnel) only one path sample is required to accurately
describe that region. The design of the cost function necessarily depends on the
sensors, robot, and the intended operating environment. The reduced set of path
samples form a list of setpoints—so termed because they are the sensor range values
the controller is attempting to achieve. To be clear, setpoints are path samples that
are used to describe the path, whereas any individual path sample may or may not
be used.
5.2 Structured Approach Using Singular Value Decomposition
To utilize the structure of the environment for control purposes, a relationship
between the sensor reading, the environment, and the robot’s motion is necessary.
Consider Figure 5.2, in which the robot is following a predefined path and has not
deviated from the path. The robot’s distance to the wall is defined as dw and the
orientation of the wall is given by 0. To determine the sensor reading, as explained
above, it is useful to place a coordinate system (reference frame) on the sensor with
the y-axis aligned with the sensing direction. This coordinate system is referred to
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Path
Wall
Figure 5.2: Determining expected sensor reading when the robot is following the
desired path.
as the sensor’s frame of reference (FOR).
In order to transform the description of the wall into the sensor’s FOR it is con
venient to use a series of transform matrices defined as:
A p
br BORG (5.2)
B
0 0 1
Br É R T - É r T APborg (5.3)
0 0 1
In Equation (5.2), is the transformation matrix which transforms a vector
described in B’s FOR to A’s FOR. is a rotation matrix which describes the
rotational mapping from B’s FOR to A’s FOR. a P borg is a vector, measured in A’s
FOR, which describes the location of B’s origin [24]. Thus, given a point’s coordinates
measured in B’s FOR, the coordinates of the point in A’s FOR is found by multiplying
the coordinates by the transformation matrix ^T. Furthermore, the transformation
matrix from reference frame A to reference frame C can be found by:
cT = ét St (5.4)
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Figure 5.3: Reference frame placement for the robot, sensor, and wall.
To apply this to the case shown in Figure 5.2 a reference frame is placed on the
wall, the robot, and the sensor (Figure 5.3). It is important to note that all the
parameters given in Figure 5.2 are measured with respect to the robot’s reference
frame; therefore, it is convenient to find all the transformation matrices for mapping
to the robot reference frame and then use (5.3) to reverse the mapping. Thus we
have:
S rr-1 R rp (5.5)
R 1 W 1
To represent the wall in the sensor’s FOR, both a point and direction are required.
Although the point used is arbitrary, it is convenient to use the origin of the wall’s
reference frame which is obtained using (5.6).
X 'O '
__ S rp 0 (5.6)
y — w 1
i 1
The direction is determined by using the rotation matrix Because the x-axis
of the wall’s FOR lies parallel to the wall, the direction of the wall (as measured in
the sensor’s FOR) is found by:
x,w (5.7)
= wR
y,™.
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sensor’s FOR is only dependent on the mounting position and orientation of the
sensor. The sensor reading when the robot deviates from the path is then:
_ dw - sx cos (SO - 0) - ôx cos ((f)) + sy sin (ôd - cf)) - ôy sin (0) , .
cos 03 +<50-0) ( )
dM ~
Now we must consider how this information is used to generate control outputs.
Following the approach outlined in Section 5.1, it is desired that the controller utilize
control setpoints, where each control setpoint is a list of the expected sensor values.
One possible approach is to use the difference between the expected, SdE, and mea
sured, SdM, sensor reading to determine the robot’s deviation from the defined path.
Taking the difference between the expected and measured sensor reading yields:
ca = cos(^) + cos(/3 — 2 0)
cb = sin(/9) — sin(/3 — 2 0)
cc = 2 (sx sin(/3) — sy cos(/3) — dw sin(/3 — 0))
cd = 2 dw cos((3 — 0)
C/ = 1 + cos(2 (0 - 0))
cg = sin(2 (f3 - </>))
_ caôx + cb Sy + cc sin(^) + cd cos(^6>) - cd
dM dE cg sm(ôQ) — Cf cos(ô6)
At this point some interesting structure is noticeable. The coefficients c are de
pendent on two things: sensor configuration and the path/environment description.
Now consider that SdM — SdE is equivalent to a standard control error signal (with
opposite sign). The setpoint contains the expected sensor reading, SdE: when the
robot follows the planned path. The measured sensor reading, SdM, are the sensor
readings measured by the robot taken at some point which may or may not be on
the planned path. By setting SdM — SdE = e and rearranging (5.11) the following is
obtained:
ca Sx + cbSy + (cc - cg e) sin(ôû) + {cd + c/ e) cos(S6) = cd (5.12)
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The equation is formatted to better show the structure. Provided the path/envi
ronment data is determined from map data, the only unknowns in the equation are
8y, and 80. Given that this equation is only for one sensor, it is apparent that
at least three sensors are necessary to resolve all the unknowns. However, this is
not necessarily a complete description as more than one wall is required for all three
variables to be solved. If only one wall is available only two variables can be com
puted, 80 and either 8x or 8y. Often only 8x will be determinant because the path is
typically parallel or near parallel to the wall (when only one wall or two parallel walls
are available). Note that these equations represent a wall as an infinite line, so any
bend or corner is minimally represented as two or more ‘walls’ within the equations.
Most frequently the robot will have more than two range-finding sensors and operate
within environments with at least two distinct walls. Thus, there is almost always
enough information to solve for these three unknowns.
To solve for these unknowns using all available sensor data, (5.12) is written for
each sensor. The corresponding system of equations may be presented in matrix form
as A'x' — b':
9
A' y -
Qz,,i Q),i Cb,i(e2) Cd,l
Ca,2 0,,2 Cb,2(e2) <%/ Cd,2
(5.13)
sin(80)
cos(80)
Ca,n Cb,n Cb,n (Cn) Cd,n
Cb (e) = cc -CgC
Q (e) = Cd + c/e
noting that the number of sensors, n, must meet the requirement n > 2. Methods
for computing the minimum least squares solution for problems of this type are well
known; however, this problem has non-linear constraints on the solution imposed by
cos(80) and sm(80). Rather than solve the non-linear problem, a straight forward
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solution is to sample several values of 66 and solve the least squares problem several
times. The 60 and corresponding x' which best minimizes \\A'x' — b'\\2 is taken as
the best solution. To accomplish this, (5.12) is rearranged to reflect the changes in
the ‘known’ values:
ca Sx + cb ôy = cd —(cc —cge) sm(ô9) — (cd-\-Cf e)cos(ô9) (5.14)
where the ‘known’ values are on the right. As before, the corresponding system of
equations is written in matrix form as Ax = b:
A x — b
Qi,i Q>,1 Cd,i - c^i(ei) sin(^) - Q,i(ei) cos(6#)
£-0,2 Q>,2 6x Cj ,2 - C/i^W) sm(<5#) - A, 2(^ 2) cos((M)
Co,n Q),n - c&,m(em) sm(<5#) - cos(6#)_
The solution to the least squares minimization is found by multiplying b by the
pseudoinverse of A. Singular value decomposition is used to compute the pseudoin
verse to avoid the poor numerical practice of computing ATA directly. Computing
the best estimate for the robot’s deviation from the planned path is accomplished
using:
A = UT,Vt
Af = V£tUT
xest = A* 6 (5.16)
The presented algorithm provides a means of computing an estimated deviation
from an assumed straight line path. However, more detail is required in order to fully
describe how this algorithm can be utilized to guide the robot along a planned path.
For instance, how is the information required by the algorithm determined? Section
5.3, which follows directly hereafter, describes how the algorithm is implemented to
realize a path-tracking controller.
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5.3 Algorithm Application
The equations developed in Section 5.2 are a mathematical description of the rela
tionship between the sensor readings, robot pose (i.e. position and orientation), and
the environment with some simplifying assumptions. The equations were developed
with the intention of determining the pose of the robot, directly relative to the desired
location within the environment. Thus the following text will focus on applying these
equations to determine the robot’s pose and the efficacy of these methods for control
purposes.
5.3.1 Data Sources
Using equations presented in Section 5.2 to determine the robot’s pose requires
several data sources for each sensor: the sensor parameters (sx, sy, and /3), the en
vironment data (dw and 0), the expected sensor reading (SdE), and the measured
sensor reading (SdM)- The sensor parameters are static for the AMR because each
sonar is mounted in a fixed position and direction which is common for many mobile
robots. The variables describing the environment, dw and 0, can be calculated from
setpoint, sensor, or map data. The expected sensor reading, SdE, is stored in the
setpoint. Finally, the measured sensor reading, SdM, is provided by the AMR while
it is navigating and sensing the environment. Of these data sources, it is prudent to
further discuss the environment variables due to the varied approaches to determining
their values.
Environment Data
The variables used to describe each wall the robot senses, dw and ÿ, are not directly
observable nor is there any easy methods to measure them. As previously mentioned,
these values can be determined either from setpoint, sensor, or map data. Accord
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estimate/measurement of </>. By using live sensor data to compute cf) we can elimi
nate errors caused by discrepancies between the map and environment. The errors in
measuring 4> from real-time sensor data is easier to quantify than the errors incurred
from the map.
Lastly, dw and (j) can be generated directly from the map data. In early versions
of the code, only the sensor data was determined from the map; but computing the
dw and (f) using the calcdwphi function is a rather indirect means of using the map to
determine the wall information. Thus the path sampling code also generates the val
ues for dw and (j) and saves them with the setpoint information. As with the estimates
obtained from expected sensor values, this approach is susceptible to differences be
tween the map and actual environment.
Position Estimation
As described in Section 5.2, the angle is sampled before computing the displace
ment values 5x and 8y with least squares minimization. How this angle is determined
is very important, because it can have a large effect on the accuracy of the position
estimate. In general, two techniques were considered for determining the angle. The
first technique is to sample a range of angles and uses the angle which yields the
smallest norm (5.16). Because sampling 89 requires the minimization problem to
be solved multiple times, this technique requires significantly more processing power.
To reduce the processing time required, the second technique determines 89 di
rectly by comparing the cj) values produced by the expected sensor readings (setpoint)
with the values obtained using real-time sensor readings. This is done by using the
calcdwphi function on both the expected and live sensor data. The result is two <p
estimates for each ‘wall’ the sensors see, one referenced from the desired position and
one from the robot’s current position. An estimate of 89 is obtained by subtracting
the observed (current) (ft from the desired <ft values and averaging the results.
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Once an estimate of the robot’s pose (Sx, Sy, SO) is known, a pose controller
is implemented to correct for the position error. The pose controller may be any
controller of suitable type (e.g. PID, fuzzy logic, et cetera). It is worth mentioning
that, from the perspective of the pose controller, the variables Sx and SO are the
error signals. The pose controller adjusts the robot’s speed and steering to drive
these error signals to zero. At this point it seems that the algorithm presented
in Section 5.2 is a type of localization algorithm, and in many ways it is similar.
But unlike localization algorithms, the robot’s position, the map, and the path are
not referenced to a common coordinate system. This algorithm localizes the robot
directly with respect to the desired path, which is itself referenced to the environment
(or walls) via the expected sensor readings. Thus this algorithm is a generalized wall-
following algorithm where the desired sensor values are provided, in a similar manner
to the wall-following controllers described in Chapter 4. Unlike those controllers,
the proposed algorithm does not distinguish between left, right, and center-following
techniques.
5.3.2 Algorithm Implementations
The different methods for calculating the environment variables and robot ori
entation were developed while coding and testing the algorithm. For example, the
slow computation time associated with sampling the robot’s orientation compelled
the author to find a better means of determining SO. This search resulted in the 0
comparison technique, which utilizes the calcdwphi function. When the speed of the
code improved remarkably, the author considered using the dw and </> (the vector nota
tion denotes the dw and (f) for all range-finding sensors) computed from the live sensor
data instead of the map generated values. Thus, several different implementations of
the algorithm were tested with different results. Three primary implementations of
the algorithm were tested in depth and are described herein.
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Figure 5.8: Simple turn simulation used for baseline comparisons.
the desired path. The pose controller used in each simulation uses a proportional-
integral-derivative (PID) controller which closes the loop around the angular (S0) and
displacement (Sx and Sy) errors estimated by the algorithm. The pose controller was
initially tuned using the ‘true’ errors computed by the simulator. A proportional
controller reduces the simulated robot’s speed when large errors occur and completes
the pose controller. Because the estimated SO affects the estimates of both Sx and
Sy, the different methods for determining SO should be carefully analyzed.
5.4.1 Angle Estimation
There were two methods tested for estimating the angle error, SO. The first method
samples different angles to determine which best minimizes ||Aà? — b\\2. The second
method compares 4>e and (/)M computed with the calcdwphi function. Figure 5.8
shows the simulation used for the baseline comparison and Figure 5.9 shows the
angle estimated for the sampling and </> comparison methods, respectively.
From the graphs it can be seen that the </> comparison method produces more
accurate SO estimates for the simulation shown. In contrast, the sampling method
underestimates the initial large displacement until the robot is within 15° of the
desired orientation. Within a range of ±15° degrees the sampling method yields
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Sampling Based 66 Estimation
0.8
— Actual 89
0.6
Estimated 86
'i/T 0.4
u
Ji o.o
< - 0.2
-0.4
0.6
-
Time (sec)
Theta Sampling at (a) Theta Sampling at (b) Theta Sampling at (c)
? H
-100 -50 100 -100 -50 0 50 100 -100 -50 0 50 100
Angle (degrees) Angle (degrees) Angle (degrees)
Figure 5.10: Least squares minimization results shown at different locations.
reasonable results except for three large spikes. These spikes in 60 estimates coincide
with changes in sensor readings going in or out of saturation; in this case, the front
sensors pointing east and northeast (assuming the front of the robot is north). In
order to better understand why the theta sampling method yields these results, a
closer look at the least squares minimization at the sampled angles is beneficial.
As shown in Figure 5.10, the results of the LS minimization at each point provide
a great deal of insight. At point a, the minimization of \\Ax — b\\2 is more sensitive to
angles in the direction of the offset. This is likely due to the increase of the incident
angle for the majority of the sensors. It is also notable that the minimum is not
definitive, the cusp of the graph is much wider than the better estimation shown at
point b.
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The minimization plot for point b is distinct and the sensitivity of the minimization
is nearly equal across the range of angles. It has been observed that the sensitivity of
the plot on either side of the minimum is related to the sign of the minimum angle. As
the minimum angle changes, the v-shape of the graph tilts. Thus when the minimum
angle is positive, the minimization graph becomes less sensitive to angular changes in
the negative direction and more sensitive to angles in the positive direction. However,
this does not necessarily hold true in environments with more complex geometry.
The minimization plot at point c has two nearly equivalent minimums. The
appropriate theta estimate should be approximately zero degrees, and it should be
noted that the minimums appear to straddle this value. The minimization graphs
at these spikes all have multiple minimum values, and although there is typically a
distinct global minimum the theta estimate is poor regardless. These results indicate
that the solution with the lowest l2 norm, with respect to the orientation, 86, is not
necessarily the best solution.
On the other hand, the (ft comparison method uses a weighted average which
is focused on reducing the effect of extreme 89 estimates. A non-weighted average
causes the 89 estimation to be sensitive to changes in sensor readings that result from
objects entering or exiting the sensor’s range. For example, Figure 5.11 shows the 89
estimation results when direct averaging is used.
As shown in Figure 5.11, as the vehicle turns to the correct heading, the wall on
the left abruptly enters the range of sonar 5 (refer to Figure 5.12 for sonar positions).
Shortly thereafter, the wall on the right exits sonar 3’s range. Between these events,
a non-existent wall is assumed to be between these two sensors. This results in an
extreme (pM estimates which skews the 89 average. The other eight sensors, however,
detect existing walls that yield coinciding angular estimates. By weighting the angle
estimates appropriately the 89 estimate is improved over direct averaging.
Now that the performance of the two 89 estimation methods has been discussed, a
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Figure 5.12: Approximate Sonar Positions on the AMR
close look at the computation of ôx and ôy is required to further explain the proposed
controller’s process.
5.4.2 Estimating Translation
A path-tracking controller cannot function without knowledge of the translational
distance to the desired path. As defined in Section 5.2, the perpendicular distance
to the desired path is expressed as ôx and the distance traveled along the path (with
respect to a path sample) is represented as ôy. An inherent problem exists while trying
to estimate ôy within a tunnel environment which is known as the ‘infinite hallway’
problem. As viewed by the range-finding sensors, there are few if any distinct features
which can be used to determine progress along the path (ôy). Because every point
along the hallway (tunnel) appears the same to the sensors, they are not sufficient to
determine distance along the path. Due to this problem, many localization algorithms
depend heavily on dead-reckoning methods in these regions. An inherent feature of
the algorithm presented in this chapter is its ability to quantify the reliability of the
ôy estimate.
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The SVD decomposition of matrix A yields the singularity matrix E, which in this
case is a 2x2 diagonal matrix containing the singular values of A. These two values
indicate how close the solution of equation 5.16 is to a singularity. Furthermore, each
of these singular values relates to how sensitive the solution is to errors within the
vector b. Finally, the matrix V indicates how sensitive the estimates of Sx and Sy are
to (7 and (72 respectively. The closer eq or a2 is to zero, the closer the solution is to
1
a singularity. For example, if the robot is traveling straight down an infinite hallway,
the matrices V and S will appear as:
"-1 o' cq 0
, E =
0 1 0 cq ~ 0
where cq is a positive non-zero number. However, because the matrix V is a diagonal
matrix for this situation, the solution for Sx is independent of a2, and thus decoupled
from the solution to Sy, which is indeterminate (infinite solutions). The least squares
solution is determined by replacing the place within Eh with zero and only
1/<j2
solving for Sx. By using the values within E and V, the ability of the algorithm to
yield stable estimates of Sx and Sy can be determined. Due to the intended use of
the algorithm for control within mine tunnels, only the value for Sx will typically be
determinant.
Figure 5.13 shows the Sx estimation corresponding to the SO estimation shown
in Figure 5.9(a). In this example, SO is estimated by sampling. The initial error of
approximately 200 mm is caused by the sampling method’s underestimation of SO
initially. The first and last ‘spikes’ in the Sx estimate are caused by the SO estimate
spikes at approximately 12 sec and 18 sec, respectively. The small error spikes between
12 sec and 18 sec are expected to be caused by a variety of error sources which will
be discussed in Section 5.4.3.
The primary Sx estimation errors incurred using Method 1 are due to errors in
estimating SO; however, accurate SO estimates do not guarantee accurate Sx estimates.
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Error in ôx Estimation
300
200
100
5 -100
-200
-300
Time (sec)
Error in 80 Estimation
^ 0.6
1 0.2
r o.o
S -0.2
M -0.4
0.6
-
Time (sec)
Figure 5.13: 5x Estimation Error using Method 1
Method 2 produces the Sx estimate shown in Figure 5.14. Although the 89 estimate
error is approximately zero for the first 12 seconds, the 8x estimate varies between
±0.15 meters in the first four seconds. Initially the algorithm estimates the vehicle
is 0.1 meters right of the desired position, this indicates a bias towards the sensors
aiming towards the left of the vehicle, which are currently reporting larger distances
on average due to the vehicle pose. This behavior only occurs if the error in the
robot’s pose has an angular component. If the robot is displaced ±1 meter to the
right or to the left of desired, the 8x estimate will be correct until the robot incurs
an angular displacement while trying to get back on path. The abrupt changes in the
error from positive to negative correspond to two of the sensors exiting saturation
(indicating a wall entered the sensors’ range). The first change corresponds to sensor
12 exiting saturation. This results in an imbalance between left/right facing sensors
being used by the algorithm, with one more right facing sensor than the left. The
algorithm’s bias shifts to the right facing sensors until sensor 5 exits saturation.
Method 3 utilizes the real-time sensor data to determine the values of dw and (j).
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Error in Sx Estimation
400
^| 300
200
r 100
Ë 0
w -100
-200
Time (sec)
Error in SO Estimation
10.2
0.1
0.0
0.2
-
Time (sec)
Figure 5.14: Sx Estimation Error using Method 2
Thus, the robot estimates the structure from its current position then determines the
best action to drive the sensor readings towards the desired values (based upon the
map). Figure 5.15 shows the Sx and SO estimation error using this method in the
simple turn simulation.
It is interesting to note that results of Method 3 do appear better than the first
two methods. The initial error is less, briefly approaching —0.15 meters in the hallway
before the turn, and limited to a maximum of -0.25 meters during the turn. There is
a great deal of similarity in the Sx estimate plots between Method 2 and 3; however,
the corresponding errors are attenuated using Method 3. As mentioned in Section
5.3, the output of Method 3 must be adjusted using (5.17) which incorporates both
Sx and Sy. It may seem unusual to incorporate Sy in the calculation, because it was
indeterminate using the first two methods within the straight hallway section. One
might expect that the attenuation is due to Sy being estimated as zero, resulting in
a loss of information. However in Method 3, V takes the form:
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Sonar 4
0 5 10 15 20
Time (sec)
Sonar 14
0 5 10 15 20
Time (sec)
Figure 5.16: Simulated sensor readings for sonar 4 and 14 in the simple turn simula
tion.
5.4.3 Error Sources
As was previously mentioned, there are a number of small estimation errors that
occur near the turn. This is because the algorithm was defined assuming straight line
paths and walls. Because the path around the turn is an arc, it must be approximated
by a greater number of path samples. The ‘true’ angular error computed by the
simulator is defined against the setpoint being used rather than the actual path. This
is why the plots are not continuous functions as one would expect—when the setpoint
changes the desired angle jumps. Another problem arises because the sensor values
change drastically within this region due to the low sampling rate.
Figure 5.16, shows the sensor values for sensors 4 and 14 (both facing right). Dur
ing the turn the incident angle between these sensors and the wall changes quickly
resulting in large changes in the distance seen by the sensor. The simulation ac
curately reflects the update rate and order of the ultrasonic sensors. New sensor
readings are taken every 50 ms with each sensor updating once every 200 ms. At
any one update, the algorithm is using anywhere between 10-12 old and 2-4 updated
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sensor values. As seen in Figure 5.16, some of the sensor readings change as much as
1 meter between updates and regularly change as much as 0.1-0.2 meters while the
robot is turning (between 12-15 seconds). Considering the errors within the sensor
readings used by the algorithm, the error in the ôx estimation, while still undesirable,
is not unreasonable.
Although each Method presented produced different estimation results, each was
able to successfully navigate the turn utilizing the pose controller described in Sec
tion 5.4. The simple turn simulation was chosen specifically to display some of the
differences between Methods, but was not the only environment/path simulated. Ap
pendix C.4 shows other environments used while developing the proposed controller.
In all of these simulations, the path was first sampled and specific path samples were
selected to be used as setpoints. How these setpoints are selected including the effect
on the performance of the proposed controller is an important factor that must be
addressed before a complete implementation can be achieved.
5.4.4 Setpoint Frequency
The Methods presented above have been compared using the accuracy of the Sx
and SO estimation within simulation. Because the ultimate goal of the algorithm
is to provide path-tracking data, it could be described as a type of feedback filter
within the control loop. Because a wide variety of control algorithms (e.g. PID, fuzzy
logic, et cetera) could be employed, the focus thus far has been to generate accurate
feedback data. However, all of the experiments thus far assume the algorithm is
being given the correct setpoints that define the path it is tracking—the role of the
localization algorithm. If the algorithm requires frequent and accurate setpoints to
perform, accurate localization becomes the primary barrier to implementation. If the
algorithm can perform with infrequent or inaccurate setpoints, the requirements of a
localization algorithm are greatly reduced.
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Consider Figure 5.17 which compares the robot’s simulated path with three differ
ent setpoints, each using the Method 2. The simulations in the first column use the
same number of setpoints as the previous simulations (n=19), the second shows n—7
setpoints, and the last shows n=l setpoints (the robot is never provided the second
setpoint shown). Interestingly the robot initially fails to navigate the turn using seven
setpoints, but succeeds using only one setpoint. Figure 5.17(g) and Figure 5.17(h)
in the first two columns shows the same simulation with a slight modification: when
the robot reaches the turn, the algorithm is fed the next setpoint rather than the
current setpoint. By providing the robot some look-ahead information, the overshoot
is nearly eliminated in both the n=19 and n—7 simulations. There is little difference
between the path taken between the two simulations; however, the accuracy of the 8x
and 89 estimations in the n—7 simulation suffers. This reflects the sparse setpoints’
inaccurate representation of the environment. The pose estimation for the n=l simu
lation is almost useless, only the region before and after the turn are accurate because
only these regions match the setpoint used. But, the ability to navigate the turn with
only one setpoint suggests the possibility to navigate with very infrequent setpoints.
Lastly consider Figure 5.18, which shows two simulations of a much more complex
environment: a hallway on the first level of the Brown Hall building on the Colorado
School of Mines campus. The first simulation uses a total of 89 setpoints and the
second simulation uses 16 setpoints (with look-ahead at the turn). In both cases the
robot successfully navigated the environment. In each case the robot will barely clip
the opposing wall at the turn due to overshoot, but the principle cause in this case
is the path and environment. The hallway itself is small compared to the AMR, and
the defined path leaves little room for error in either defining or following the path.
From these simulations it is clear that the algorithm can be tolerant to sparse,
and thus often inaccurate, setpoints if some adjustments are made to provide look
ahead information in particular regions. The ability to navigate with sparse setpoints
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benefits the MineSENTRY project. The lower the setpoint frequency, the smaller the
burden on the localization algorithm. Consequently, the map detail and computing
power required is also reduced. Lower-detail maps will be easier to update on-the-fly
which is likely to be necessary in a mine disaster situation. Furthermore, reduced
computing power allows the use of smaller, low-watt age processing technology. The
benefits of this are not immediately apparent unless the logistics of the MineSEN
TRY system are considered. The deeper communications are extended into the mine
the greater the number of AMRs required. It would be difficult to transport and
launch a large number of golf-cart-sized robots. However, smaller robots can carry
less on-board power which also limits available processing power. The extent to which
the proposed algorithm can fulfill these statements will largely depend on the per
formance of a full implementation. For example, the effect the setpoints have on the
performance of the simulated algorithm/controller requires further investigation. An
improved cost function for choosing setpoints could greatly improve the performance
of the proposed algorithm, but will require far more testing and analysis beyond the
scope of this thesis.
5.5 Summary
The algorithm presented is a unique approach to the path-tracking control prob
lem due to the unique combination of the environment, path, and sensor information
into a single equation. The solution to this equation provides the tracking infor
mation necessary for the robot to follow the path within a mine environment. Un
like the guidance controllers of Chapter 4, the proposed algorithm is a generalized
wall-following approach that does not specifically distinguish between left, right, and
center-following techniques.
Several methods of implementing the proposed algorithm were tested in simula
tion. The results indicate that the algorithm can provide accurate pose estimation
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CHAPTER 6
CONCLUSIONS
The MineSENTRY research project set out to develop a multi-robot system that
solves the problems associated with wireless communication in mining environments.
To create this system, a single Autonomous Mobile Relay was created using a golf
cart as the vehicle platform. AMR construction required a number of subsystems to
be designed, built, and tested. Proof-of-concept testing, conducted at the Colorado
School of Mines’ Edgar mine, confirmed the effectiveness of the MineSENTRY design,
but the AMR guidance controllers were lacking. A unique controller was developed
as a proposed solution to the guidance problem. The controller was tested using
a variety of simulations. Although the simulations yield promising results, further
development is necessary before the controller can be implemented on the AMR.
6.1 Recommendations for Future Work
With respect to the AMR robotic platform detailed in Chapter 3, the modified
golf cart performed admirably well as a test platform. The primary recommendations
for improving the AMR system are itemized below:
• Improve protection from the environment
— Cover steering servo system.
— Paint/cover steel components.
— Seal electronic/sensor enclosures.
• Improve actuator assemblies
— Add easily accessible lever for steering disconnect.
— Replace brake actuator with higher power unit.
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• Improve sonar reliability.
As shown in Chapter 4, the sonars can provide very reliable data within the mine.
It is expected rather simple adjustments could be made to greatly improve reliability.
The controller proposed in Chapter 5 requires some work before a full implemen
tation can be realized. However, relatively little work is required before live exper
imentation can begin. It is recommended that future work focus on the following
topics:
• Improving the pose estimation during turns.
• Improving sensor-based calculations for dw and <j) (calcdwphi function).
• Analyze the effect of setpoint frequency and an associated method for choosing
setpoints.
• Developing a method to choose setpoint during navigation.
• Testing how poor map data affects controller.
In particular, a means of choosing the correct setpoint is necessary for any large-
scale experiments. Small-scale experiments could be performed nearly immediately
for single setpoint paths, although some code alteration would be required before the
controller could be used outside of the simulator. As a final recommendation, efforts
to streamline and optimize the AMR control software would be very beneficial.
6.2 Implications of Research
The controller proposed in Chapter 5 has potential to provide mobile robot nav
igation at a lower cost. By incorporating the environment structure into the con
troller the need for accurate localization is reduced. In turn, this lowers the need
for expensive sensor systems and high performance computing. With respect to the
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for the VC to fully enter autonomous control mode. The AC’s query packet requests
sensor data, and the command packet requests a control action as well as sensor data.
The VC responds to both the query and command packets with a sensor response
packet, which includes the sensor data requested. The emergency stop packets for
both controllers are unique.
If the AC sends an emergency stop (set E-stop) packet to the VC, the VC will
cut throttle and allow the AMR to coast. The VC will return a status packet in
response. The AC uses another emergency stop (release E-stop) packet to release the
E-stop condition. The VC’s emergency stop packet is the only packet the VC will
send without prompting. The VC sends an emergency response packet to the AC to
indicate that a mechanical E-stop on the AMR has been pressed. The VC reacts the
same way to both a software (AC) E-stop and a mechanical E-stop. The E-stop can
only be released by releasing the source of the E-stop (i.e. Mechanical E-stops cannot
release software E-stops or vice-versa).
The data is transferred via an RS-232 synchronous data stream with 8 bits, 1
start bit, 1 stop bit, and no parity at 57600 Baud. All data is represented with ASCII
characters - numbers are represented in hexadecimal format followed by the carriage
return character. Please note, the following tables are common to the MineSENTRY
project, and thus may appear in other MineSENTRY documentation and Theses.
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Autonomous Controller to Vehicle Controller Communication
Note on notation: ‘uint 8’ denotes an unsigned 8 bit integer.
Table C.l: Vehicle Controller to Autonomous Controller Header (common for all
packets)
Byte # Name Value Format Unit Description
0:7 Start Sequence “{start:}” string N/A The start sequence is used in case one
side is not in sync with the other.
8 Packet Type 0 uint 8 N/A This field indicates that the packet is
a system packet.
9 Length X uint 8 N/A Length of the packet in bytes, exclud
ing the start sequence, packet type,
and length fields.
Table C.2: Vehicle Controller to Autonomous Controller System Status Packet
Byte # Name Value Format Unit Description
0:7 Start Sequence “{start:}” string N/A The start sequence is used in case one
side is not in sync with the other.
8 Packet Type 0 uint 8 N/A This field indicates that the packet is
a system packet.
9 Length X uint 8 N/A Length of the packet in bytes, exclud
ing the start sequence, packet type,
and length fields.
0x00: Manual Control
10 Mode See description uint 8 N/A 0x01: Radio Control
0x10: Autonomous Control
0x00/0x01: Brake engaged
0x00/0x02: Softbump Front
0x00/0x04: Softbump Rear
0x00/0x08: SC Reset
11:12 Status See description uint 8 N/A 0x00/0x10: Invalid RC Signal
0x00/0x20: SC non-responsive
0x00/0x40: Vehicle too slow
0x00/0x80: FWD/Reverse
0x01/0x00: Throttle saturation
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Table C.3: Vehicle Controller to Autonomous Controller Sensor Response Packet
Byte # Name Value Format Unit Description
0:7 Start Sequence “{start:}” string N/A The start sequence is used in case one
side is not in sync with the other.
8 Packet Type 0 uint 8 N/A This field indicates that the packet is
a system packet.
9 Length X uint 8 N/A Length of the packet in bytes, exclud
ing the start sequence, packet type,
and length fields.
10:12 Response Fields OxXXXXXX uint 8 N/A Which fields hold data
x:28 Sonar 0—6000 uint 16 mm 14 sonar readings
x:8 IR 0—6000 uint 16 mm 4 IR Readings
x:4 HE 0—255 uint 8 dm/s 4 velocity readings
x:2 Odometer 0—65535 uint 16 m Odometer reading
1 SP -60—60 int 8 deg String Pot Steering Angle
1 Throttle 0—100 uint 8 N/A Throttle Setting (x %)
1 Brake 0—100 uint 8 N/A Brake Setting (x %)
0x00: Manual Control
10 Mode See description uint 8 N/A 0x01: Radio Control
0x10: Autonomous Control
0x00/0x01: Brake engaged
0x00/0x02: Softbump Front
0x00/0x04: Softbump Rear
0x00/0x08: SC Reset
11:12 Status See description uint 8 N/A 0x00/0x10: Invalid RC Signal
0x00/0x20: SC non-responsive
0x00/0x40: Vehicle too slow
0x00/0x80: FWD/Reverse
0x01/0x00: Throttle saturation
Table C.4: Vehicle Controller to Autonomous Controller Emergency Packet
Byte # Size Name Value Format Unit Description
0:7 2 Start Sequence “{start:}” string N/A The start sequence is used in case one
side is not in sync with the other.
2 1 Packet Type OxFF uint 8 N/A This field indicates that the packet is
a query packet.
3 1 Length 1 uint 8 N/A Length of the packet, excluding the
start sequence, packet type, and length
fields.
1 Emergency See uint 8 N/A 0x00: Remove Estop
4
Stop Description 0x01: Set E-stop
Autonomous Controller to Vehicle Controller Communication
Table C.5: Autonomous Controller to Vehicle Controller Header (common for all
packets)
Byte # Name Value Format Unit Description
0:7 Start Sequence “{start:}” string N/A The start sequence is used in case one
side is not in sync with the other.
8 Packet Type 0 uint 8 N/A This field indicates that the packet is
a system packet.
9 Length X uint 8 N/A Length of the packet in bytes, exclud
ing the start sequence, packet type,
and length fields.
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ABSTRACT
Anaerobic, passive biological treatment can provide effective removal of heavy
metals, including uranium, from waters influenced by mining activities. Sulfate-reducing
bacteria (SRB) contained within the organic substrate of jhese systems are able to
reduce sulfates in water to sulfides, allowing for the formation and precipitation of metal
sulfides. SRB also have the ability to enzymatically reduce uranium from its soluble,
mobile state as U(VI) to its insoluble, immobile state as U(IV). Passive systems relying
on treatment by SRB require less maintenance and are generally less expensive than
traditional active chemical precipitation methods. These features make anaerobic,
passive systems appealing for treatment of effluent waters at remote or abandoned mine
sites.
Seven pilot scale anaerobic bioreactors were constructed to treat uranium
contaminated effluent from the Lower Fair Day Mine. To avoid large reductions of
hydraulic conductivities in the substrates over time, Profile soil amendment was mixed
with composted steer manure in different ratios by volume. This study tested the metal
removal capacity of mixtures containing 10% and 20% Profile by volume, as well as all
manure substrates for a control. Prior to the use of the substrates in the field system,
the substrate bulking agent mixtures were tested in column reactors in the laboratory.
The column experiments were designed to determine the hydraulic conductivity of each
substrate mixture. Data gathered from the column study could be used to choose which
substrate mixture ratios were best to use in the field scale system.
A battery of laboratory tests was performed on samples of the substrate bulking
agent mixtures. Samples of unused, column, and field substrates were analyzed to
determine physical changes in the mixtures when used for different periods of time. The
tests on the substrate samples provided data necessary to determine the hydraulic
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properties of each reactive mixture. The laboratory analysis provided the specific
gravity, bulk density, organic content and particle size distribution for each substrate
mixture. Using this information the porosity and hydraulic conductivity could be
calculated and compared for each mixture over time.
In addition to the anaerobic treatment system, a series of aerobic settling pools
were installed at the Fair Day Mine site. Filtered and unfiltered water samples were
collected from the effluent of each bioreactor as well as the lowest settling pool. This
allowed for the direct comparison of the aerobic and anaerobic treatment of the mine
effluent. Data gathered from this comparison may provide information useful for
selecting treatment technologies in future mine remediation projects.
All of the substrate bulking agent mixtures used in the field reactors provided
treatment of the Fair Day effluent. The anaerobic bioreactors outperformed the
treatment provided by the aerobic settling pools for uranium, zinc, manganese, and
cadmium. The aerobic settling pools provided better aluminum removal than the
bioreactors. While the treatment efficiencies provided by both systems were variable,
the fluctuations in treatment efficiencies occurred during the same time periods for the
reactors and settling pool. This indicates that changes in treatment efficiencies can be
attributed to the rate of metal loading to the mine effluent.
The addition of Profile soil amendment did increase the porosity of the substrate
mixtures. Profile also helped to increase the initial hydraulic conductivities of the
substrates, as well as to limit the reduction of hydraulic conductivity in the reactors over
time. Substrate mixtures amended with Profile were not negatively affected in terms of
metal treatment capacities. Amending the composted steer manure with Profile did
reduce the organic content of the mixture. However, the benefits Profile offers for
improving the hydraulic properties of the system outweigh its negative impacts, making
Profile an effective substrate amendment for anaerobic passive systems.
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