Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller

arXiv:2404.01184v1 Announce Type: cross
Abstract: Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficult to use in real time. To address this issue, we propose a new generalizable control barrier function (CBF)-based steering controller to reduce the number of samples needed in a sampling-based motion planner RRT. Our method combines the strength of CBF for real-time collision-avoidance control and RRT for long-horizon motion planning, by using …

arxiv control cs.lg cs.ro function motion planning planning type