基于人工势场法的多智能体编队避障方法

编队避障问题是多智能体编队研究的关键问题之一。针对动态环境中多智能体编队避障问题，提出了一种基于人工势场法（APF）与布谷鸟搜索算法（CS）相结合的编队避障方法。首先，在动态队形变换策略的异构模式下，利用APF为多智能体编队中每个智能体规划避障；然后，针对APF在引力增量系数和斥力增量系数设置的局限性，利用CS中的莱维飞行机制思想，来随机搜索得到适应环境的增量系数。Matlab仿真实验结果表明，所提方法能够有效地解决复杂环境下多智能体编队避障问题，使用效率函数对实验数据进行评价及分析，验证了所优化方法的合理性和有效性。

Obstacle avoidance method for multi-agent formation based on artificial potential field method

Formation obstacle avoidance is one of the key issues in the research of multi-agent formation. Concerning the obstacle avoidance problem of multi-agent formation in dynamic environment, a new formation obstacle avoidance method based on Artificial Potential Field (APF) and Cuckoo Search algorithm (CS) was proposed. Firstly, in the heterogeneous mode of dynamic formation transformation strategy, APF was used to plan obstacle avoidance for each agent in multi-agent formation. Then, in view of the limitations of APF in setting attraction increment coefficient and repulsion increment coefficient, the idea of Lěvy flight mechanism in CS was used to search randomly for the increment coefficients adapted to the environment. The simulation results of Matlab show that, the proposed method can effectively solve the obstacle avoidance problem of multi-agent formation in complex environment. The efficiency function is used to evaluate and analyze the experimental data, which can verify the rationality and effectiveness of the proposed method.