Dynamic Path Planning for Mobile Robots Based on Improved RRT* and DWA Algorithms

Abstract

The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown dynamic environments. In this article, we propose a dynamic path planning scheme by combining the improved RRT* and the improved dynamic window approach (DWA). For pregenerating an initial path, we use the artificial potential field (APF) method to expand new nodes. The adaptive dynamic step-size is introduced for accelerating the optimal path searching. Moreover, the improved ant colony algorithm is used to perform multiobjective optimization on the generated initial path. When unknown obstacles appear in the path, the improved DWA is developed for obstacle avoidance. Finally, the proposed method is validated by simulation and experiment in both of the static and dynamic environments. In particular, the simulation results show that, compared with some existing methods, our algorithm can generate a higher-quality initial path in the static environment and avoid unknown dynamic obstacles effectively in the dynamic environment. Furthermore, we implement our algorithm in a mobile robot to verify the correctness and effectiveness in the practical scenario.