Multi-robot flocking path planning based on fusion of APF and Improved DWA algorithms

Robot f1ocking systems in unknown environments have higher adaptability, higher efficiency and stronger robustness compared to single robots. In this paper, a control algorithm based on the fusion of the artificial potential field algorithm and the improved dynamic window algorithm is proposed for the path planning in the multi-robot flocking control process. In the free space, the multi-robot flocking will obtain the velocity control input based on the artificial potential field algorithm, and go to the target point quickly with the "quasi-lattice" formation structure; when the robots senses obstacles, control algorithm switches to the dynamic window algorithm, and obtains the control input that can maintain the flocking state and realize the obstacle avoidance at the same time, according to the evaluation function. In this paper, the orientation evaluation function is improved, and a new evaluation functions are added to form an evaluation function with four evaluation indexes, which solves the robot local optimization problem and ensures the formation state of the robots, and improves the excellent trajectory score. The validity and advantages of the proposed algorithm are verified through theoretical analysis and simulation experiments.