A Robotic Excavator for Autonomous Truck Loading.
Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavators software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.
Introduction
The surface mining of metals, quarrying of rock, and construction
of highways require the rapid removal and handling
of massive quantities of soil, ore, and rock. Typically,
explosive or mechanical techniques are used to pulverize
the material, and digging machines such as excavators load
the material into trucks for haulage to landfills, storage ar-
eas, or processing plants. As shown in Figure 1, an excavator
sits atop a bench and loads material into trucks that
queue up to its side. The operator is responsible for designating
where the truck should park, digging material from
the face and depositing it in the truck bed, and stopping for
people and obstacles in the loading zone.

Figure

1. Excavator loading a truck with soil in a typical mass
excavation work scenario.
The opportunities for automation are immense. Typical-
ly, loading a truck requires several passes, each of which
takes 15 to 20 seconds. Reducing the time of each loading
pass by even a second translates into an enormous gain
across the entire job. The operator's performance peaks
early in the work shift and degrades as the shift wears on.
Scheduled idle times, such as lunch and other breaks, also
diminish average production across a shift. All of these factors
are areas where automation can improve productivity.
Safety is another opportunity. Excavator operators are
most likely to be injured when mounting and dismounting
the machine. Operators tend to focus on the task at hand
and may fail to notice other site personnel or equipment entering
the loading zone. Automation can improve safety by
removing the operator from the machine and by providing
complete sensor coverage to watch for potential hazards
entering the work area.
Numerous researchers have addressed aspects of automated
earthmoving (Singh, 1997). The lowest and most
common level of automation has been teleoperation. Typi-
cally, the operator is removed from the scene for reasons of
safety. Teleoperated excavators are used in applications
that pose a danger to humans, such as the uncovering of
buried ordnance (Nease and Alexander, 1993) and waste
(Burks et al., 1992; Wohlford et al., 1990), or excavation
around buried utilities. A higher level of autonomy is
achieved by systems that share control of the excavation
cycle with a human operator. Typically, these systems
(Bradley et al., 1993; Bullock and Oppenheim, 1989; Huang
and Bernold, 1994; Lever et al., 1994; Rocke, 1994; Sakai
and Cho, 1988; Salcudean et al., 1997; Sameshima and
Tozawa, 1992; Seward et al., 1992) concentrate on the process
of digging. An operator chooses the starting location
for the excavator's bucket and a control system takes over
the process of filling the bucket using force and/or joint position
feedback to accomplish the task. At the next level of
autonomy are systems that automatically select where to
dig. Such systems measure the topology of the terrain using
ranging sensors (Feng et al., 1992; Singh, 1995; Takahashi
et al., 1995) and compute dig trajectories that maximize excavated
volume. At the highest level of autonomy are systems
that sequence digging operations over a long period
(Bullock et al., 1990; Romero-Lois et al., 1989; Singh,
1998).
The prior work addresses many subproblems important
for autonomous truck loading, however in order to field a
fully automated system that performs at the level of its
manually operated equivalent, a much broader set of problems
must be solved than just digging. Sensors are needed
to sense the dig face, recognize and localize the truck, and
detect obstacles in the workspace. Perception algorithms
are needed to process the sensor data and provide information
about the work environment to the planning algo-
rithms. Planning and control algorithms are needed to
decide how to work the dig face, deposit material in the
truck, and move the bucket between the two.
We have developed a complete system for loading trucks
fully autonomously with soft materials such as soil. The
Autonomous Loading System (ALS) was implemented and
demonstrated on a 25-ton hydraulic excavator and succeeded
in loading trucks as fast as an expert human operator.
The rest of the paper describes the ALS and presents results
from experimental trials.
2. System Overview
The Autonomous Loading System uses two scanning laser
rangefinders that are mounted on either side of the boom
(see

Figure

to sense the dig face, truck, and obstacles in
the workspace. Two scanners are needed for full coverage
of the workspace and to enable concurrent sensing opera-
tions. Each sensor has a sample rate of 12 kHz, and a motorized
mirror sweeps the beam circularly in a vertical
plane. Additionally, each scanner can pan at a rate of up to
degrees per second, enabling this circle to be rotated
about the azimuth, as shown in Figure 3. The scanner positioned
over the operator's cab is called the ``left scanner'',
and it is responsible for sensing the workspace on the left
hand side of the excavator. The "right scanner", which is
located at a symmetric position on the right side of the
boom, is responsible for sensing the workspace on the right
hand side of the excavator.

Figure

2. Sensors mounted on excavator.

Figure

3. Two axis scanning sensor configuration.
The excavator uses its scanners in the following fashion
when loading a truck (Figure 4). While the excavator digs
its first bucket, the left scanner pans left from the dig face
across the truck both to detect obstacles and to recognize,
localize, and measure the dimensions of the truck. Using
this information, a desired location in the truck to dump the
material is planned, and the bucket swings toward the
truck. During this swing motion, the right scanner pans left
across the dig face to measure its new surface, and the next
location to dig is calculated. The right scanner continues to
pan toward the truck. After the soil is dumped into the
truck, the right scanner pans back across the dig face to detect
obstacles in the way of the implements. The excavator
swings back to the next dig point. During this swing, the
left scanner pans across the truck to measure the soil distribution
in the truck bed, and the next desired dump location
is calculated. This process repeats for each subsequent
loading pass until the truck is full, with the exception that
truck recognition is only necessary for the first pass for
each new truck. Typically, six passes are needed to load our
twenty-ton truck with our excavator testbed.
Left scanner
Right
scanner
Pan axis
Vehicle cab
Rotating
reflector
Distance
Scan axis
Obstacle or terrain
Scan plane
sensor
measuring

Figure

4. Top view of sensor configuration.
Information from the scanners is processed using an on-board
array of four MIPs processors. The software architecture
is shown in Figure 5. The boxes are software modules
that can run on one of the system processors. Circles
are hardware components such as sensors. Lines represent
communication channels. The sensor interfaces receive
data from the two scanners and control the panning motion
of the devices. Sensor data from the interfaces are passed to
scanline processors, where they are converted from spher-
ical, sensor coordinates to Cartesian, world coordinates using
corresponding data from the position system. These
three-dimensional range points are then made available to
whatever perception software modules require them.
One consumer of this processed sensor data is the truck
recognizer, which recognizes the truck and measures both
its dimensions and location. Two others are the dig point
planner, which plans a sequence of dig points for eroding
the dig face, and the dump point planner, which plans a sequence
of dump points for loading soil into the truck bed.
The digging motion planner controls the excavator during
digging at the specified location. The dumping motion
planner dumps the bucket of soil into the truck and returns
to the dig face. The sensor motion planner controls the panning
for both scanners to coordinate scanner and excavator
motion, following the scenario described above. The obstacle
detector processes sensor data from the scanner that is
sweeping in advance of excavator's motion and stops the
machine if an obstacle is detected in its path. The machine
controller interface communicates commands to the low
level machine joint controller, which executes the commands
and sends excavator state information back to the
planning modules.

Figure

5. ALS software architecture.
3. Hardware Subsystem
The ALS hardware subsystem consists of the servo-controlled
excavator, on-board computing system, perception
sensors, and associated electronics. In this paper we focus
on the perceptual sensors which provide the data from
which the truck is identified, the dig location is chosen, obstacles
are avoided, and ultimately the mass excavation
process is achieved.
With the target application of earthmoving, we focussed
on developing a laser based scanning system that would be
able to penetrate a reasonable amount of dust and smoke in
the air. The laser itself would need to be able to accurately
measure range from a variety of target materials (e.g., met-
als, wood, dirt, rock, snow, ice, and water), colors and tex-
tures. We also needed a system that would be robust to dust
and dirt accumulating on the protective "exit" window
(glass or plastic which protects the laser and optics from
weather and dirt, though permits the beam to pass).
Over the past decade, a variety of laser based scanners
have been produced. With the exceptions of the Dornier
(Shulz, 1997) and Schwartz (Schwartz) scanners, most
have either been research devices or limited to indoor us-
age. None that we know of addresses the problems of dust
penetration or a partially occluded (i.e., dirty) exit window.
We have developed two different time-of-flight scanning
ladar systems that are impervious to ambient dust condi-
tions. The first uses a "last-pulse" technique that observes
the waveform of the returned light and rejects early returns
that can arise from internal reflections off of a dirty exit
Left sensor
Right sensor
Dig face
Truck
scan plane
scan plane
Right
scanner
Left scanner
left sensor
interface
right sensor
interface
left scanline
processor
right scanline
processor
left
sensor
right
sensor
position
system
truck
recognizer
dig point
planner
dump point
planner
digging
motion
planner
dumping
motion
planner
sensor
motion
planner
machine
controller
interface
obstacle
detector
excavator
states commands
position data
sensor data sensor data
sensor data
sensor data
dig pt.
dump pt.
truck info.
sensor
sensor
commands
states
sensor
data
to obst. det.
window, or from a dust cloud obscuring the target (see Figure
6). In general, the next-to-last pulse returns are due to
dust in the scene and are indicative of what a normal "first
pulse" rangefinder would see. For instance, in Figure 6, a
first pulse rangefinder would detect the dirty exit window
and would be unable to "see" the target. Even if the window
were clean, the first pulse unit would still "see" the
dust cloud instead of the target. Since reflections off the
exit window are rejected with the last pulse technique, the
unit can be environmentally sealed using an inexpensive
transparent cover that does not have to be optically perfect
or clean. Another advantage is that the laser system can
also report when multiple returns occur, giving a warning
that dust is present. This is important because overall ranging
reliability and accuracy is decreased in dusty condi-
tions, so an autonomous machine might need to adopt a
slower, more conservative motion strategy.

Figure

6. Last pulse detection concept.

Figure

7. Trailing edge detection of target when target is
obscured in dust cloud.
There is, however, a limitation to last-pulse rangefind-
ing. When the target is within the dust cloud, the receiver
electronics can have difficulty separating the dust and target
returns (see Figure 7). We have built a second dust penetrating
scanner system that identifies that target by
locating the "trailing edge" of the last return signal as is
shown in Figure 7. Like the last pulse system, this device is
also robust to occlusions on the exit window making it ideal
for construction and mining environments. Though the
trailing edge detection technique forgoes some range accu-
racy, we believe it is a superior approach for environments
where the dust may frequently surround the target.

Figure

8. Last pulse vs. trailing edge detection when target is
within dust cloud.
The television monitor pictured in Figure 8 shows range
points plotted from a single scanline for both the last pulse
and trailing edge scanners. Range increases from the left to
the right of the monitor. The top monitor screen shows
scans of the rear of a dump truck. The bottom screen shows
scans of the same truck but shrouded in a heavy dust cloud.
Note that the last pulse device is unable to separate the dust
cloud from the truck and reports the front of the cloud. The
trailing edge device correctly reports range to the truck regardless
of the presence of dust.
It is important to note that both dust penetrating techniques
are physically limited by very heavy dust levels that
attenuate the return target signal below the point of detectability

4. Software Subsystem
The software subsystem consists of several software modules
that process sensor data, recognize the truck, select
digging and dumping locations, move the excavator's
joints, and guard against collision. In this section, the algorithms
employed by key software modules in the software
architecture are described.
4.1. Truck Recognition
In order to properly load a truck, an excavator operator
must verify that it is a loadable vehicle, determine its loca-
tion, and determine its dimensions. This information is essential
for calculating a loading strategy and for planning
the sequence of joint motions that implements this strategy.
Dirty exit window Dust cloud Target
Laser
source
Threshold
(using last pulse)
Return
signal
Target range
Dirty exit window Dust cloud Target
Laser
source
Return
signal
Threshold
(w/trailing edge)
Target range (w/last pulse)
In some scenarios, such as surface mining, the loaders are
serviced by a mine-owned fleet of haulage trucks. An automated
system could acquire this information by equipping
each truck with a global positioning system (GPS) sensor
and an identification transponder. However, in other scenarios
such as highway construction, the loaders are serviced
by a variety of independently-owned, on-highway
trucks of varying dimensions, so equipping each and every
truck with such sensors could be infeasible. For such sce-
narios, an automated system could acquire the necessary
information using rangefinder data.

Figure

9. Raw range data of a truck.

Figure

10. Truck model fit to segmented data.
The truck recognizer uses sensor data to automatically
recognize, localize, and dimension haulage trucks. As the
excavator digs its first bucket of soil, the left scanner pans
across the truck, which is assumed to be parked to the ex-
cavator's side. The raw sensor data are shown in Figure 9.
Each rotation of the mirror returns one vertical scanline of
data, created by intersecting a vertical plane with the truck.
Each scanline is processed into line segments which are
grouped with coplanar line segments from other scanlines
to form planar regions.
Using an interpretation tree approach (Grimson, 1990),
the simple model for a truck bed, shown in Figure 10, is
matched to the segmented data region by region. Depth-first
search is used to hypothesize model-to-scene region
matches. At each level in the tree, constraints are used to
prune the search and to check for consistency with previously
hypothesized matches. The interpretation that matches
most of the model regions and survives the verification
stage is selected as the correct one. In order for the truck
recognizer to recognize a class of truck models rather than
just a single model, the model in Figure 10 uses parameter
ranges rather than single parameter values. Ranges are used
on the sizes of the planar regions in the model, the locations
of their centroids relative to each other, and the angles between
the planes. These parameter ranges are checked for
consistency at every level in the interpretation process to
prune the search. This specification allows the truck recognizer
to identify trucks of varying sizes and truck bed
shapes.
For each complete interpretation (i.e. an attempt to match
all model regions to scene regions), the truck recognizer
performs a verification. The verification consists of finer-grained
consistency checking of truck parameters, and the
identification of the four "corner points" in the sensor data
that define the opening of the truck bed. For the selected in-
terpretation, the corner points are used to calculate the position
and orientation of the truck bed. This information is
passed to other modules in the system for producing a
dumping strategy. Figure 10 shows the model matched to
the planar regions segmented from the raw sensor data.
4.2. Coarse-to-Fine Dig Point Planning
Automated earthmoving operations such as leveling a
mound of soil are distinguished from typical planning
problems in two important ways. First, soil is diffuse and
therefore a unique description of the world requires a very
large number of variables. Second, the interaction between
the robot and the world is very complex and only approximate
models that are also computationally tractable are
available. The large state space and complex robot-world
interaction imply that only locally optimal planners (i.e. per
dig) can be created. In order to deal with the practical issues
of excavating large volumes of earth in applications, we
have developed a multi-resolution planning and execution
scheme. At the highest level is a coarse planning scheme
that uses the geometry of the site and the goal configuration
of the terrain to plan a sequence of "dig-regions." In turn,
each dig region is searched for the "best" dig that can be executed
in that region. Finally, the selected dig is executed
by a force based closed loop control scheme (Rocke, 1994).
Treatment of the problem at three levels meets different ob-
jectives. The coarse planner ensures even performance over
a large number of digs. The refined planner chooses digs
that meet geometric constraints (reachability and colli-
sions) and which locally optimize a cost function (e.g. vol-
ume, energy, time). At the lowest level is a control scheme
that is robust to errors in sensing the geometry of the terrain

Figure

11 shows the process of coarse to fine planning
for the excavator.

Figure

11. Coarse to fine planning strategy.

Figure

12. Coarse plan for an excavator.
The coarse planner takes as input processed sensor data
which it places in a terrain map (a 2-D grid of height val-
ues). The output is a sequence of dig regions, each of which
is in turn sent to a refined planner. Figure 12 shows a strategy
for removing material that was recommended by an expert
excavator operator. Each box indicates a region, and
the number within the box indicates the order in which the
region is provided to the refined planner. In this strategy,
material is removed from left to right, and from the top of
the face to the bottom. There are several reasons for choosing
this strategy. In most cases, the truck is parked on the
operator's left hand side so that the operator has an unobstructed
view of it. By digging from left to right, the implements
do not need to be raised as high to clear material
when swinging to the truck. In digging from top to bottom,
less force is required from the implements because it is not
necessary then to work against the weight of the material
up above. In addition, clearing material away from the top
minimizes the range shadows cast on the face of the terrain
given a scanning range sensor that is mounted on the cab.
The refined planner operates on an abstract representation
of an atomic action (i.e. a single dig). Rather than
searching for a bucket trajectory, the refined planner
searches through compact task parameters within the
bounds specified by the coarse planner. In order to select
the best digging action, the refined planner evaluates candidates
through the use of a forward model that simulates
the result of choosing an action (in our case the starting location
of the bucket). An evaluation function scores the trajectory
resulting from each action, and the action that meets
all constraints and optimizes the cost function is chosen.
This process is shown in Figure 13.

Figure

13. Operation of refined planner.
4.3. Template Based Dump Planning
The truck must be loaded evenly and completely. Because
of uncertainty in soil settlement, the dumping strategy may
need to be revised for each successive bucket load. The
dump point planner applies a template-based approach to
robustly find the low regions of soil distribution in the truck
bed.
Sensor data are gathered after each bucket of soil has
been dumped in the truck as the excavator is swinging back
to the dig face. Like the dig point planner, the sensor data
are placed in a 2-D terrain map. The dump point planner
also requires information about the location of the truck,
provided by the truck recognizer module, so it can filter out
any irrelevant sensor data that are outside of the truck bed.
The terrain map is then smoothed using a simple Gaussian
filter to eliminate any sensor noise. The current grid cell
resolution of the truck bed terrain map is 15 cm, with a typical
map containing on the order of 500 cells.
Occlusion of the deposited soil by the truck bed walls is
a serious problem. Rather than assuming that nothing is in
the unseen regions of the truck bed, the dump point planner
fills in any unknown grid cells with the average elevation
of the known grid cells. This results in some slight inaccuracies
in the perceived soil distribution at first, but they diminish
as more soil is placed in the truck bed.
Finally, a specific terrain shape template is convolved
Coarse
Planner
Refined
Planner
Closed Loop
Executor
Region
Goal
Candidate
for each dig:
(starting
Constraints
(kinematics,
coarse plan
Digs
bucket angle,
location)
Forward Dig
Model Evaluation
swept volume,
trajectory,
over the entire truck bed terrain map to produce a score for
each grid cell. This small 5x5 or 7x7 grid cell template
looks for a certain profile of the material in the truck bed,
such as a slope or a hole. Simple templates of constant elevations
can be used to find the lowest elevation in the truck
bed terrain map as well. The convolution operator produces
a score which represents how well the template matched
the particular region in the truck bed, and the location of the
cell with the best score is returned as the desired dump location

4.4. Script Based Motion Planning
The motion planning software coordinates the motions of
the excavator's joints for each loading pass, beginning immediately
after digging a bucket of soil and ending when
the bucket has returned to the next dig point. The main objectives
of the motion planner are to plan motions which
place the soil at the desired dump location, avoid all known
obstacles in the workspace such as the truck, and execute
each loading cycle as quickly as possible.

Figure

14. Truck loading script for an excavator.
Because of power constraints and joint coupling effects
of the excavator's hydraulic system, as well as the difficulty
in accurately modeling the dynamics of such a machine,
more traditional optimal trajectory generation schemes do
not work well. Instead, recognizing the fact that the exca-
vator's motions are highly repetitive and very similar from
loading cycle to loading cycle, and that it operates in a relatively
small portion of its total workspace, a script based
approach to motion planning was adopted (Rowe and
Stentz, 1997). A script is a set of rules which define the
general motions of the excavator's joints for a certain task,
in this case loading trucks. These rules contain a number of
variables, known as script parameters, which get instantiated
on every different loading pass.
The rules of script were designed with the input of an expert
human excavator operator and implicitly constrain
what the excavator is and is not allowed to do. For example,
if it was advised that moving two particular joints simultaneously
was a bad idea, then the rules of the script make
that motion impossible. The left hand side of the rules are
functions of the excavator's state, and the right hand side of
the rules are the commands which the planner sends to the
excavator's low level joint controllers. Thus, when the left
hand side of a particular rule evaluates to true, its corresponding
command gets sent to the excavator. The rules get
re-evaluated at a fixed rate, 10 Hz for example, during the
execution of the excavator's motion.

Figure

14 shows the script rules for the truck loading
task. The numbers in boldface are one example set of script
parameters, which will be described in more detail below.
The q's are the excavator's state, in this case the angular
positions of the joints. The commands are desired angular
joint positions. Notice that each joint has its own separate
script. Therefore, only one rule per joint may be active at a
time.
The script parameters are computed before each loading
pass starts using the information about the truck's location
and the desired dig and dump points. There are two types
of script parameters, those which appear in the left hand
side of the script rules and affect which commands are sent
by the planner, and the joint commands themselves which
appear on the right hand side of the rules.
The command script parameters in the right hand side of
the rules are primarily computed by geometric and kinematic
means. For example, consider the command of
from step 1 of the boom joint's script in Figure 14. That is
the boom angle which is required for the excavator's bucket
to safely clear the top of the truck, and is a kinematic
function of the height and location of the truck relative to
the excavator. Similarly, the stick joint commands are computed
using knowledge about the radial distance of the
truck from the excavator, and the swing joint's commands
are found from the desired dig and dump points.
The script parameters in the left hand side of the rules are
found through a combination of simple excavator dynamic
models and heuristics. These simple dynamic models capture
first order effects of the excavator's closed loop behavior
when given desired angular position commands. These
models provide information about the velocities, accelera-
tions, and command latencies for each joint, which are used
to intelligently coordinate the different joint motions, resulting
in faster loading times. As an example, consider the
case when the excavator has finished digging, and the
bucket is raising up out of the ground. The excavator does
not need to wait until the bucket has raised to its full clearance
height before swinging to the truck. Instead, it can begin
swinging at some earlier point as the bucket is still
Joint 1: Swing
digging finishes, wait
swing to truck
swing to dig
Joint 3: Stick
digging finishes, wait
move to spill point
move to dump point
move to dig
Command
Command
Joint 2: Boom
digging finishes, raise
lower to dig
Joint 4: Bucket
digging finishes, curl
move to dig
Command
Command
raising, but it must have the knowledge provided by the dynamic
models about how much time it will take to swing to
the truck and to raise the bucket so it can safely couple the
two motions to avoid a collision.
4.5. Obstacle Detection
A major requirement for automated loading is detecting
and stopping for people and other obstacles which pose a
threat for collision. Obstacle detection software has been
developed which uses sensor data to perceive objects in the
excavator's workspace, and simple dynamic models to predict
where the excavator's linkage will be for a short time
in the future as the excavator swings back and forth between
the dig face and the truck. The predicted excavator
linkage locations are compared to the sensor data, and if
there is an intersection, the excavator is immediately commanded
to stop. It is crucial that the sensors scan far
enough ahead of the excavator's motion, and the prediction
is far enough in the future, for the excavator to have enough
time and space to come to a complete stop and avoid hitting
the obstacle. This look-ahead distance is a function of the
swing joint's maximum velocity and was found through
experimentation to be between 40 to 50 in front of the ex-
cavator's swing joint.
The prediction of the excavator's location is done using
the simplified models of the excavator's closed loop dynamic
behavior. Not only is the obstacle detection algorithm
predicting what the excavator itself will do, it must
also simulate what the motion planner will do using the
predicted excavator state. It performs this prediction at the
same rate of the script rule base update, 10 Hz for instance.
The final result is a list of predicted excavator linkage
states for some amount of time. The look-ahead time was
found empirically to be between 2 - 3 seconds.

Figure

15. Depiction of the points that are calculated on the
underside of the linkage.
For each predicted linkage state, the coordinates of
points on the envelope underneath the linkage are comput-
ed. This is done using the forward kinematics of the excavator
and simple linear models of the shapes of the
linkages. This is shown in Figure 15. Each point on the underside
of the linkage for each predicted linkage state is
then compared to the 2-D elevation map of sensor data. If
any point on the underside of the linkage is lower than the
elevation of the grid cell that coincides with it, then a predicted
collision is reported and the excavator is commanded
to stop.

Figure

16. Typical dig for truck loading.
5. Results

Figure

shows the excavator after digging a bucket of
soil, and Figure 17 shows the truck after it has been loaded
with six buckets of soil. To date, we have autonomously
loaded our truck hundreds of times. The typical loading
times are 15 to 20 seconds per pass, with six passes needed
to load the truck. This rate is very close to the loading times
logged by an expert operator manually loading trucks in the
same configuration using the same excavator.

Figure

17. Truck is loaded after six passes.
6. Conclusion
We have demonstrated an autonomous loading system for
excavators which is capable of loading trucks with soft ma-
underside
linkage
envelope
terial at the speed of expert human operators. The system
uses two scanning laser rangefinders to recognize the truck,
measure the soil on the dig face and in the truck, and to detect
obstacles in the workspace. The system modifies both
its digging and dumping plans based on settlement of soil
as detected by its sensors. Expert operator knowledge is encoded
into templates called scripts which are adjusted using
simple kinematic and dynamics rules to generate very
fast machine motions. We believe ours to be the first fully
autonomous system to load trucks for mass excavation.

Acknowledgments

This paper summarizes the work of the Autonomous
Loading System team. This team consists of Stephannie
Behrens, Scott Boehmke, Howard Cannon, Lonnie Devier,
Frazier, Tim Hegadorn, Herman Herman, Alonzo
Kelly, Murali Krishna, Keith Lay, Chris Leger, Oscar Lu-
engo, Bob McCall, Ryan Miller, Richard Moore, Jorgen
Pedersen, Chris Ravotta, Les Rosenberg, Wenfan Shi, and
Hitesh Soneji in addition to the authors.



--R


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A Laboratory Study of Force-Cognitive Excavation
Force and Geometry Constraints in Robot Excavation.
Remote excavation using the telerobotic small emplacement excava- tor
Research on Control Method of Planning Level for Excavation Robot.
Object Recognition by Computer: The Role of Geometric Constraints
Control Model for Robotic Backhoe Excavation and Obstacle Handling.
Robotics for Challenging Environments.
Intelligent Excavator Control for a Lunar Mining System.
A Strategic Planner for Robot Excavation.
Operation System for Hydraulic Excavator for Deep Trench Works.

Impedance control of a teleoperated mini excavator
of the 8th IEEE International Conference on Advanced Robotics (ICAR)
Development of Auto Digging Controller for Construction Machine by Fuzzy Logic Control.
Schwartz Electro-optics Inc
Imaging Ladar Camera for Washing Robots.
Synthesis of Tactical Plans for Robotic Excava- tion
State of the Art in Automation of Earthmoving


Autonomous shoveling of rocks by using image vision system on LHD.
New capability for remotely controlled excavation.
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Joaqun Gutirrez , Dimitrios Apostolopoulos , Jos Luis Gordillo, Numerical comparison of steering geometries for robotic vehicles by modeling positioning error, Autonomous Robots, v.23 n.2, p.147-159, August    2007
laser rangefinder;manipulator;dig planning;autonomous excavation;software architecture;robotic excavator;integrated robotic system