Field-enriched A* search algorithm for robot motion in path planning
Dongyue Lei · Applied and Computational Engineering · 2024
Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.