Informed Asymptotically Near-Optimal Planning for Field Robots with Dynamics

Abstract

Recent progress in sampling-based planning has provided performance guarantees in terms of optimizing trajectory cost even in the presence of significant dynamics. The STABLE_SPARSE_RRT (SST) algorithm has these desirable path quality properties and achieves computational efficiency by maintaining a sparse set of state-space samples. The current paper focuses on field robotics, where workspace information can be used to effectively guide the search process of a planner. In particular, the computational performance of SST is improved by utilizing appropriate heuristics. The workspace information guides the exploration process of the planner and focuses it on the useful subset of the state space. The resulting Informed- SST is evaluated in scenarios involving either ground vehicles or quadrotors. This includes testing for a physically-simulated vehicle over uneven terrain, which is a computationally expensive planning problem.