Robot obstacle avoidance path planning based on improved artificial potential field
Lingyun Zhu, Hailong Xie, Ziyang Song · 2023
In the realm of intelligent agent control, path planning has been one of the most well-liked study subjects. The path planning for obstacle avoidance approach proposed in this study is an enhanced artificial potential field method. By directing the barrier in the robot's direction and combining the boundary repulsion and vector decomposition of the goal direction, the local minimum problem is resolved. Next, the final combined force is delivered to the robot's center and the angle coefficient is added to provide a fair obstacle avoidance effect while taking into account the physical distance between the two sides of the robot and the quantity of extra environmental obstacles. Finally, the enhanced algorithm is used to perform a global path search. The simulation results validate the applicability and effectiveness of the suggested approach.