Improving the Efficiency of Sampling-based Motion Planners via Runtime Predictions for Motion-Planning Problems with Dynamics

Abstract

While sampling-based approaches have made significant progress, motion planning with dynamics still poses significant challenges as the planner has to generate not only collision-free but also dynamically-feasible trajectories that enable the robot to reach its goal. To improve the efficiency of sampling-based motion planners, this paper develops a framework, termed Motion-Planning Runtime Prediction (MPRP), that relies on machine learning to train models to predict the expected runtime of a planner. When solving a new motion-planning problem, the trained model is then incorporated into the motion planner to more effectively guide the search toward parts of the state space that are associated with low expected runtime predictions. This paper applies the MPRP framework to state-of-the-art sampling-based motion planners to obtain new planners, which are shown to be significantly faster.