Anytime Kinodynamic Motion Planning using Region-Guided Search
Matthew Glen Westbrook, Wheeler Ruml · 2020
Many kinodynamic motion planners have been developed that guarantee probabilistic completeness and asymptotic optimality for systems for which steering functions are available. Recently, some planners have been developed that achieve these properties of completeness and optimality without requiring a steering function. However, these planners have not taken strong advantage of heuristic guidance to speed their search. This paper introduces Region Informed Optimal Trees (RIOT), a sampling-based, asymptotically optimal motion planner for systems without steering functions. RIOT's search is guided by a low-dimensional abstraction of the state space that is updated during planning for better guidance. Simulation results suggest RIOT is adaptable, scalable, and more effective on difficult problems than previous work.