Efficient hierarchical global motion planning for autonomous vehicles

Motion planning has become an increasingly important task as autonomy in mechanical systems has gained in popularity. Such systems must be able to plan new trajectories and controls reliably and rapidly in response to inputs. The challenges in creating such planners are decidedly non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased computational complexity though symmetry and a hybrid systems representation of the dynamics, (b) utilization of local controllability through a local planning algorithm, (c) minimization of a cost functional, (d) a randomized planner for obstacle avoidance, and (e) convergence guarantees. Applications include autonomous vehicles, sensor-based planning, and multiple vehicle coordination in ground-based, underwater, atmospheric, and orbital environments.