An improved RRT-based motion planner for autonomous vehicle in cluttered environments

Abstract

In this paper, we present an improved RRT-based motion planner for autonomous vehicles to effectively navigate in cluttered environments with narrow passages. The planner first presents X-test that can identify passable narrow passages, and then perform an efficient obstacles-based extension operation within passable narrow passages. In order to generate a smooth trajectory for the vehicle to execute, a post-process algorithm with trajectory optimization is proposed. For the purpose of demonstrate benefits of our method, the proposed motion planner is implemented and tested on a real autonomous vehicle in cluttered scenarios with narrow passages. Experimental results show that our planner achieves up to 13.8 times and 7.6 times performance improvements over a basic RRT planner and a Bi-RRT planner respectively. Moreover, the resulting path of our planner is more smooth and reasonable.