基于模糊人工势场法的多智能体编队控制及避障方法

针对动态环境中多智能体编队控制及避障问题,提出了一种基于模糊人工势场法的编队方法。首先,在领航跟随法的框架下控制编队队形,在动态队形变换策略的异构模式下,使用人工势场法为多智能体编队中每个智能体规划避障路径;其次,利用模糊控制器控制跟随智能体追踪领航智能体,同时保持跟随智能体之间与领航智能体的相对距离,遇到未知障碍物时,及时保持多智能体编队之间的队形并避免碰撞障碍物。针对人工势场法在引力增量系数和斥力增量系数设置的局限性,利用模糊控制器选择出适应环境的增量系数。Matlab仿真实验结果表明,该方法能够有效地解决复杂环境下多智能体编队控制及避障问题,使用效率函数对实验数据进行分析,验证了所优化方法的合理性和有效性。

A multi-agent formation control and obstacle avoidancemethod based on fuzzy artificial potential field method

Aiming at the problem of multi-agent formation control and obstacle avoidance in dynamic environments, we propose a formation method based on fuzzy artificial potential field method. Firstly, the method uses the artificial potential field method to plan the path of obstacle avoidance for each agent in the multi-agent formation under the heterogeneous mode of controlling the shaped formation and the dynamic formation transformation strategy under the framework of the leader-follower method. Secondly, the fuzzy controller is used to control the follow-up agent to track the leader agent while maintaining the relative distance between the following agent and the piloting agent. When encountering unknown obstacles, the original multi-agent formation is preserved and the obstacles are avoided in time. Aiming at the limitation of the artificial potential field method in setting of gravitational increment coefficients and repulsive force increment coefficients, the fuzzy controller is used to select incremental coefficients so as to adapt to the environment. The MATLAB simulation experiments show that the proposed method can effectively solve the problems of multi-agent formation control and obstacle avoidance in complex environments, and analysis on experiment data by the efficiency function verifies the rationality and effectiveness of the optimization method.