State Space Sampling of Feasible Motions for High Performance Mobile Robot Navigation in Highly Constrained Environments

Abstract

Sampling in the space of controls or actions is a well-established method for ensuring feasible local motion plans. However, as mobile robots advance in performance and competence in complex outdoor environments, this classical motion planning technique ceases to be effective. When environmental constraints severely limit the space of acceptable motions or when global motion planning expresses strong preferences, a state space sampling strategy is more effective. While this has been clear for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. This paper presents an effective algorithm for state space sampling based on a model-based trajectory generation approach. This method enables high-speed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense off-road obstacle fields.