An Effort Bias for Sampling-based Motion Planning

Recent advances in sampling-based motion planning have exploited concepts similar to those used in the heuristic graph search community, such as computing heuristic cost-to-go estimates and using state-space abstractions to derive them. Following this trend, we explore how the concept of search effort can be exploited to find plans quickly. Most previous work in motion planning attempts to find plans quickly by preferring states with low cost-to-go. Recent work in graph search suggests that following search effort-to-go estimates can yield faster planning. In this paper, we demonstrate how this idea can be adapted to the context of kinodynamic motion planning. Our planner, BEAST, uses estimates of effort that are learned on-line to guide the expansion of a motion tree toward states through which a plan is estimated to be easy to find. We present results with four different simulated vehicles (car, hovercraft, blimp and quadrotor) in six different environments indicating that BEAST is able to find solutions much more quickly and has a higher success rate than previous methods. We see this work as further strengthening the algorithmic connections between motion planning and heuristic graph search.