Collaborative Multi-Robot Formation Control and Global Path Optimization

For multi-robot cooperative formation and global path planning, we propose to adjust the repulsive field function and insert a dynamic virtual target point to solve the local minima and target unreachability problems of the traditional artificial potential field (APF) method. The convergence speed and global optimization accuracy of ant colony optimization (ACO) are improved by introducing improved state transfer functions with heuristic information of the artificial potential field method and optimizing the pheromone concentration update rules. A hybrid algorithm combining APF and improved ACO is used to calculate an optimal path from the starting point to the end point for the leader robot. A cooperative multi-robot formation control method combining graph theory and consistency algorithm is proposed based on path planning of the leader robot. Taking AGVs in a logistics distribution center as an example, the feasibility of the improved algorithm and its effectiveness in solving the multi-robot path planning problem are verified.

Automated summary

In this paper, an improved artificial potential field with a dynamic virtual target point and ant colony optimization are combined for multi-robot cooperative formation and global path planning. By adjusting the repulsive field function and introducing a dynamic virtual target point, the local minima and target unreachability problems of the traditional method are solved, resulting in improved convergence speed and global optimization accuracy of the algorithm.