An Improved Informed-RRT* algorithm for Mobile Robot Path Planning
Chuang Li, Yuhao Yuan, Zhongwan Tan, Yilin Chen · 2024
Aiming at the limitations of the Informed Rapidly-exploring Random Tree (Informed-RRT*) algorithm in mobile robot path planning—specifically, its lack of goal orientation and directionality, inefficient searching, and lengthy planning times— this paper proposes an enhanced path planning algorithm that integrates the Artificial Potential Field (APF) method with the Informed-RRT* approach. Firstly, a probabilistic adaptive goal-biasing strategy is introduced to reduce search randomness. Secondly, the artificial potential field method is incorporated into the path extension process to reduce the required number of iterations. Additionally, a dynamic growth step size, based on obstacle density, is applied during the path generation phase to prevent local optima. Lastly, a bisection method is employed to create parent nodes, thereby enhancing path quality. Simulation results indicate that the proposed algorithm significantly improves both efficiency and path quality over the standard Informed-RRT* algorithm.