Exploiting collisions for sampling-based multicopter motion planning
Jiaming Zha, Mark W. Mueller
Abstract
Multicopters with collision-resilient designs can operate with trajectories involving collisions. This paper presents a sampling-based method that can exploit collisions for better motion planning. The method is built upon the basis of the RRT* algorithm and takes advantages of fast motion primitive generation and collision checking for multicopters. It generates collision states by detecting potential intersections between motion primitives and obstacles, and connects these states with other sampled states to form collision-inclusive trajectories. We show that allowing collision helps improve the performance of the sampling-based planner in narrow spaces like tunnels. Finally, an experiment of tracking the trajectory generated by the collision-inclusive planner is presented.