An Efficient Path Replanning Technique for EST-Based Robot Navigation in Dynamic Environments

Abstract

Expansive-Spaces Trees (EST) is a single-query sampling-based path planner. When EST is applied to robot navigation in dynamic environments, EST confronts the risk of a collision with dynamic obstacles since it has generated a path without any consideration for dynamic obstacles. This paper proposes an efficient path replanning technique for EST-based robot navigation in dynamic environments. The proposed technique replans a collision-free and efficient path instead of the original EST path which may cause a collision with dynamic obstacles. Besides, the replanned path can be easily merged into the original EST path because it preserves the property of EST. Simulation results in various dynamic environments reveal that the proposed technique can successfully replan a collision-free and efficient path for EST-based navigation.