Industrial Robot Path Planning Based On Immune Algorithm Research

With the increasing popularity and development of industrial robot technology in automatic production field, automation production and processing enterprises demand for more efficient and more accurate industrial robot path planning. At present, some small and medium enterprises still use inefficient manual teaching method on industry robot path planning which directly affects the enterprise production efficiency and quality. According to this problem, our research content is to make path planning for industrial robot according to improved immune algorithm and make the path planning safer, more effective, faster, and more intelligent.In the paper, clonal selection algorithm (CSA) is researched as the theoretical basis. For its shortcomings, an improved adaptive mutation operator is presented which adjusts antibody mutation probability according to the accessibility; An improved selection operator is proposed by taking accessibility and concentration into account. The improvements of the robot path planning based on the ICSA are:a new antibody encoding method based on grid nodes is raised. It can simplify coding length and increase the calculation speed. The number of robot movement direction increases from eight to360degree in modeling based on grid method. So the path length will be shortened, and the number of the path corner will be decreased. The path will be more smooth. Considering the path length and smoothness, a new fitness function is designed for path antibody which can improved the algorithm convergence efficiency and optimization result.In this paper, an industrial robot path planning simulation platform is designed under Matlab7.0. We adopt CSA and ICSA simulation experiment in the same environment. The results indicate that ICSA is superior to CSA on convergence success rate, efficiency, computation efficiency and convergence optimal. And ICSA can quickly search out the optimal collisionless path for industrial robot.