基于混沌变异粒子群算法的工业机器人轨迹规划OA

To address the problem of multi-constraint motion in robots within industrial production,a trajectory planning method based on the chaotic mutation particle swarm optimization algorithm was proposed to optimize motion parameters under multiple constraints.The particle swarm optimization algorithm was enhanced by adopting an initialization method that integrated Logistic chaotic mapping with dynamic reverse learning.The elite construction strategy of the K-neighborhood model and an improved rou-lette strategy were introduced,considering both local and global spatial search.A dynamic mutation neighborhood search strategy was employed to increase population diversity and improve the likelihood of escaping local optima.Benchmark function optimiza-tion tests and performance comparisons with other algorithms demonstrated that the proposed method offered high-quality solutions.In the application of trajectory planning for industrial robots using quintic polynomial interpolation,the method was found to effectively reduce acceleration variations in each robot joint,while satisfying constraints on working time and motion speed,thus significantly improving the robot's motion stability compared to the traditional particle swarm and existing improved algorithms.