人工免疫算法在机器人路径规划中的研究

Research on Artificial Immune Algorithm in Robot Path Planning

针对传统免疫算法应用于机器人路径规划中,存在的早熟收敛、容易陷入局部最优及后期收索能力减弱等缺陷,本文引入克隆选择算子,按照种群中抗体亲和度的大小进行选择克隆,并对抗体采用高频变异的方式进行改进。使用Matlab搭建机器人路径规划仿真平台,在栅格地图环境下,对传统算法和改进算法进行对比仿真分析,仿真结果表明改进后的人工免疫算法,有效地避免了局部收敛,提高了收敛速度,所规划路径更接近最优路径,并且扩大了搜索范围,改善了局部搜索能力差等问题。验证了本文所提改进方案的可行性和有效性。

Aiming at the shortcomings of traditional immune algorithm applied to robot path planning, such as premature convergence, easy to fall into local optimum and weakening of late acquisition ability, this paper introduces clonal selection operator and selects clone according to the affinity of antibody in the population. The improvement of the antibody using high frequency variation is also made. The Matlab is used to build the robot path planning simulation platform. In the grid map environment, the traditional algorithm and the improved algorithm are compared and simulated. The simulation results show that the arti？cial immune algorithm is improved, which avoids local convergence and improves the convergence speed. The planning path is closer to the optimal path, and the search range is expanded, and prob-lems such as poor local search ability are improved. Verify that the improve-ments proposed in this paper is feasible and effective.