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<p>This work introduces a new information-centric pseudospectral optimal control based  algorithm for autonomous trajectory planning and control of unmanned ground  vehicles with real-time information updates. It begins with a comprehensive study and  comparison of the various path planning methods currently in use. It then provides an  analysis of the optimal control method, including vehicle and obstacle modeling  techniques, several different problem formulations, and a number of important insights  on unmanned ground vehicle motion planning. The new algorithm is then utilized on a  collection of motion planning scenarios with varying levels of information; the  performance of the planner and the solution accuracies under these varying levels of  information are studied for both single and multi-vehicle scenarios. The multi-vehicle  scenarios compare and contrast centralized, decentralized, decoupled, coordinated,  cooperative, and prioritized control methods. Finally, the versatility of the planner (and  the optimal control technique) is demonstrated, as it is used as both a path follower and  trajectory planner in a collection of scenarios, including multi-vehicle formations and  sector keeping.
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<br>Subjects: Real-time programming.; Optimal control; pseudospectral; autonomous trajectory planning; unmanned ground vehicles; real-time; path planning; DIDO</div>
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Hurni, Michael A.</div>
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