Adaptive Hybrid PSO–APF Algorithm for Advanced Path Planning in Next-Generation Autonomous Robots

The field of autonomous robotics is progressing rapidly, with research moving toward developing systems capable of moving without direct human control and learning without human intervention. One of the problems requiring an efficient and sustainable solution is ensuring the smooth and safe navigation of robots between obstacles. In this study, a new path planning approach is developed, integrating particle swarm optimization (PSO) and artificial potential field (APF) algorithms to assist the mobile robot in navigating an area with static and dynamic obstacles. The robot moves independently while routing dynamically and avoiding obstacles. To evaluate its adaptive ability to a changing environment, we continuously calculate the shortest distance between two points and dynamically adjust the path to avoid obstacles during replanning, path recalculation, and robot position adjustment to ensure efficient and safe navigation. Different scenarios are tested to evaluate our approach, including different environmental conditions and obstacle configurations. Experimental results show that our method reduces the path length by 18%, the obstacle avoidance efficiency by 90%, and the success rate by 85% in dynamic environments. In addition, PSO-APF reduces computation time, demonstrating better capacity and efficiency.