Multi-Agent Formation Control in Complex Environments Based on Artificial Potential Field and Leader-Follower Strategy

This paper proposes an enhanced artificial potential field (APF) method that integrates the Leader-Follower strategy with the Grey Wolf Optimization (GWO) algorithm for multi-agent formation control in complex environments. Traditional APF methods often struggle with local minima and inefficient obstacle avoidance due to fixed gain coefficients, while Leader-Follower strategies may fail in dynamic settings. The proposed method overcomes these challenges by dynamically optimizing the attractive and repulsive gain coefficients using GWO, improving both obstacle avoidance efficiency and formation stability. Additionally, a differential-drive control model and a virtual ideal follower are introduced to enhance coordination and maintain stable formations. Simulations demonstrate that the proposed method outperforms traditional APF and Leader-Follower approaches in terms of obstacle avoidance, convergence speed, and formation stability, making it a more effective solution for multi-agent coordination in complex, dynamic environments.