Efficient and Asymptotically Optimal Kinodynamic Motion Planning via Dominance-Informed Regions

Abstract

Motion planners have been recently developed that provide path quality guarantees for robots with dynamics. This work aims to improve upon their efficiency, while maintaining their properties. Inspired by informed search principles, one objective is to use heuristics. Nevertheless, comprehensive and fast spatial exploration of the state space is still important in robotics. For this reason, this work introduces Dominance-Informed Regions (DIR), which express both whether parts of the space are unexplored and whether they lies along a high quality path. Furthermore, to speed up the generation of a successful successor state, which involves collision checking or physics-based simulation, a proposed strategy generates the most promising successor in an informed way, while maintaing properties. Overall, this paper introduces a new informed and asymptotically optimal kinodynamic motion planner, the Dominance-Informed Region Tree (DIRT). The method balances exploration-exploitation tradeoffs without many explicit parameters. It is shown to outperform sampling-based and search-based methods for robots to significant dynamics.