An Improved Method For Multi-agent Systems Avoiding Obstacle Based On Flocking Algorithm

In the traditional flocking algorithm, it is basically assumed that the properties of each agent are the same. In this paper, based on the flocking algorithm proposed by Olfati-Saber, the problem of individual differences of the agent is investigated. It is also found that when agents encounter a narrow intersection, it can fall into a local optimal solution causing stagnation due to the balance of the artificial potential field, so the obstacle avoidance of the flocking algorithm is improved to enable the agents to pass through such terrain smoothly and to reach convergence again after passing the terrain. It is demonstrated that the improved algorithm can perform the obstacle avoidance function through suitable parameter selection. Finally, computer simulations are given to verify the feasibility of the algorithm.