Flow Field-Based Enhanced Artificial Potential Field Method: Enhancing Cooperative Search and Obstacle Avoidance in Swarm Robotic

Abstract In the context of cooperative search and obstacle avoidance for a swarm of robots using heuristic optimization algorithms, the traditional artificial potential field method often leads to oscillating robot trajectories and excessive dispersion of the robot swarm. To address these issues, this study proposes an improved artificial potential field method based on a flow field as the obstacle avoidance approach for cooperative search in a swarm of robots. This method incorporates the robot's safety range and dynamically adjusts the repulsive potential field of obstacles to reduce the negative impact of inefficient obstacle avoidance on search efficiency. Additionally, an adaptive inertia weight adjustment method is designed for the robots, which considers the essence of robot updates and aims to smooth the robot's search trajectory and promote consensus building within the robot swarm. Finally, the proposed algorithm is validated through simulations and compared with relevant algorithms in different environments to demonstrate its effectiveness and efficiency.