基于改进粒子群算法的工业机器人模块化轨迹规划优化方法  

Modular Trajectory Planning Optimization Method for Industrial Robots Based on Improved Particle Swarm Optimization

作　　者：张和平 Zhang Heping(Qingdao Boye Electromechanical Equipment Co.,Ltd.,Qingdao,Shandong 266000,CHN)

机构地区：[1]青岛铂烨机电设备有限公司,山东青岛266000

出　　处：《模具制造》2025年第5期178-180,共3页Die & Mould Manufacture

摘　　要：针对工业机器人轨迹规划面临的复杂性和高效性挑战,提出了一种基于改进粒子群算法(PSO)的模块化轨迹规划优化方法。该方法通过引入自适应惯性权重、模糊控制学习因子调整和Cauchy变异策略来增强PSO算法性能,并结合任务分解实现轨迹参数化建模及多目标优化。实验验证了所提方法在提高轨迹精度、运动平稳性和执行效率方面的显著优势。Aiming at the complexity and efficiency challenges of trajectory planning for industrial robots,a modular trajectory planning optimization method based on improved particle swarm optimization(PSO) was proposed.This method introduces adaptive inertia weights,fuzzy control learning factor adjustment and Cauchy variation strategy to enhance the performance of PSO algorithm,and combines task decomposition to achieve trajectory parametric modeling and multiobjective optimization.Experiments show that the proposed method has significant advantages in improving trajectory accuracy,motion smoothness and execution efficiency.

关 键 词：工业机器人 粒子群优化 轨迹规划 模块化优化 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]