Self-learning RRT* Algorithm for mobile robot motion planning in complex environments

Abstract

RRT* is a practical and efficient incremental sampling-based motion planning algorithm. However, its searching ability is quite inefficient in some cases, due to relying on uniform random sampling like other RRT-based algorithms without taking the environment information and prior knowledge into account, which particularly leads to many sampling failures or generation of useless nodes in complex environments. In this paper, we propose an extension of RRT* based on a self-learning strategy and a hybrid-biased sampling scheme to improve the planning efficiency. By taking advantage of the prior knowledge accumulation and cost estimation, the searching tree has higher probability and success rate to extend in difficult areas. We also demonstrate the performance of our algorithm by building some simulation environments for our mobile robot and conclude with the results compared with RRT*.