Optimal Sensor-Based Motion Planning for Autonomous Vehicle Teams
Technical Report,29 Sep 2009,31 Mar 2017
Naval Postgraduate School Monterey United States
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Autonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasuresMCM. A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algorithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful framework for solving these types of motion planning problems with dynamic constraints and different performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertaintygeneralized optimal control GenOCthus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for different MCM search missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patternsespecially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal motion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.