<div class="description">
<p>This work introduces the use of optimal control methods for simultaneous target
sequencing and dynamic trajectory planning of an autonomous vehicle. This is achieved
by deriving a control solution that minimizes a cost to maximize target selection. In the
case of varying target priorities, the method described here preferentially selects higher
priority targets to maximize total benefit in the allowed time horizon. Traditional
techniques employ heuristic techniques for target sequencing and then apply a separate
trajectory planning process to check the feasibility of the sequence for the given vehicle
dynamics. This work uses pseudospectral methods to deterministically solve the problem
in a single step. The historic barrier to the application of optimal control solutions to the
target sequencing problem has been the requirement for a problem formulation built from
continuous constraints. Target points are, by their nature, discrete. The key breakthrough
is to transform the discrete problem into a continuous representation by modeling targets
as Gaussian distributions. The proposed approach is successfully applied to several
benchmark problems. Thus, several variations of the so-called motorized travelling
salesman problem, including ones in which it is impossible to acquire all the targets in the
given time, are solved using the new approach.
</p>
<br>Subjects: Optimization; Optimal Control; Travelling Salesman Problem; Orienteering Problem; Mission Planning; Path Selection; Target Selection; Target Priorities; Pseudospectral Methods; DIDO</div>
<div class="fn value">
<dl><dd>Greenslade, Joseph Micheal</dd>
<dd>Karpenko, Mark</dd></dl></div>
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