Asymptotical RRT-Based Path Planning for Mobile Robots in Dynamic Environments

Abstract

Rapidly-exploring random trees(RRT) is an important approach in motion planning. However, the uniform sampling strategy in conventional RRT methods leads to a low sampling efficiency in some situations and the solution is sub-optimal. Besides, conventional methods usually do not apply to dynamic environments. This paper presents an asymptotical RRT based path planning method for mobile robots in dynamic environments. To increase the sampling efficiency, a heuristic sampling algorithm is proposed on the basis of the prior knowledge of the previous planning results and the potential field of the environment. In addition, a post-processing procedure is adopted to optimize the path in consideration of path length and safety. Besides, a replanning strategy is presented to deal with the moving obstacles in dynamic environments. The simulation result shows that the heuristic sampling method has a higher sampling efficiency and faster rate of convergency than uniform sampling, a feasible path can be generated in consideration of safety and smoothness.