Autonomous Robot Navigation using Advanced Motion Primitives

Abstract

We present an approach to efficient navigation of autonomous wheeled robots operating in cluttered natural environments. The approach builds upon a popular method of autonomous robot navigation, where desired robot motions are computed using local and global motion planners operating in tandem. A conventional approach to designing the local planner in this setting is to evaluate a fixed number of constant-curvature arc motions and pick one that is the best balance between the quality of obstacle avoidance and minimizing traversed path length to the goal (or a similar measure of operation cost). The presented approach proposes a different set of motion alternatives considered by the local planner. Important performance improvement is achieved by relaxing the assumption that motion alternatives are constant-curvature arcs. We first present a method to measure the quality of local planners in this setting. Further, we identify general techniques of designing improved sets of motion alternatives. By virtue of a minor modification, solely replacing the motions considered by the local planner, our approach offers a measurable performance improvement of dual-planner navigation systems.