Robot motion planning benchmarking and optimization through motion planning pipeline
Shuai Liu, Pengcheng Liu · 2021
Motion planning algorithms have been designed with adaptability to different problems. However, how to choose a suitable planner for a scene has always been a question worth exploring. This paper aims to find a suitable motion planner under two different scenes and three different queries. The work lies in optimization of sampling-based motion planning methods through Motion Planning Pipeline and Planning Request Adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking, which determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, although the planning time of the algorithm is longer, the generated path quality is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, the performance of the two optimization methods may not be as good as the original algorithm.