Robust Path Planning for Quadruped Robots in Dynamic and Challenging Environment

This paper proposes a path planning method for quadruped robots that combines an improved Jump Point Search (JPS) algorithm with the Artificial Potential Field (APF) method. Existing global path planning methods are unable to avoid dynamic obstacles while ensuring an optimal route. Local planning methods, such as APF, encounter issues like unreachable goals and local optima. To address these challenges, we propose a hybrid method that combines the globally-oriented JPS algorithm with the locally-oriented APF approach. To enable dynamic obstacle avoidance while maintaining a relatively optimal path, thus making it more suitable for real-world environments. We rigorously prove the stability of this fusion algorithm. Simulation results verify the effectiveness of the proposed method.