为了解决复杂环境中移动机器人的路径规划问题，结合人工势场法计算量小的特性和人工免疫网络的自适应调节能力，提出了一种改进的路径规划免疫算法。为了提高免疫网络的搜索能力以及免疫网络的收敛性，将人工势场法的规划结果作为先验知识构建了导向权，同时将抗体命令清晰度和抗体转移后的距离变化作为变量，构建了新的抗体转移概率算子。仿真结果表明，与其他算法相比，新算法在最优规划能力和网络收敛性能方面都有明显提高。

To solve the path planning problem of mobile robots in complicated environments, combining the small computational amount of artificial potential field and the adaptive regulation ability of artificial immune network, an improved immunity path planning algorithm is presented. To further improve the search ability and convergence of immune network, the planning results of artificial potential field are taken as the prior knowledge to construct the guidance weight, and the antibody instruction definition and distance changes after the antibody transition are taken as the variables to construct the antibody transmission probability operator. Compared with the correlative planning algorithms, simulation results indicate that the optimal planning ability and network convergence of proposed algorithm is highly improved.