基于CPSO优化算法的六自由度机械臂轨迹规划  

Trajectory Planning of 6-DOF Manipulator Based on CPSO Optimization Algorithm

作　　者：汤小红[1,2] 任垒垒 张宏 龚永健 TANG Xiao-hong;REN Lei-lei;ZHANG Hong;GONG Yong-jian(School of Mechanical and Electrical Engineering,Central South University of Forestry and Technology,Changsha 410004,China;School of Mechanical Engineering,Hunan Institute of Science and Technology,Hengyang 414006,China)

机构地区：[1]中南林业科技大学机电工程学院,长沙410004 [2]湖南理工学院机械工程学院,岳阳414006

出　　处：《组合机床与自动化加工技术》2023年第3期59-62,65,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：长沙市重点研发计划(Kq2004094)。

摘　　要：针对焊接机器人的多目标优化问题,综合考虑时间、能耗、脉动冲击工作指标,提出一种具有混沌寻优的多目标自适应粒子群优化算法(CPSO)。在工作轨迹数学模型采用五次NURBS曲线插值机械臂末端路径点,把混沌寻优的方法融合进粒子群优化中,并提出采取动态加权方法进行选择适应度最高的全局最优粒子;通过外部存档改进粒子群的自适应变异规则,进而产生更加优异的粒子,降低局部最优的可能性,提高运算效率,改进了Pareto取值集合空间。通过软件仿真,在已知约束条件下,该优化算法可以实现目标轨迹的多目标优化,保证焊接机械臂以最优路线运行。Aiming at the multi-objective optimization problem of welding robot,a multi-objective adaptive particle swarm optimization(CPSO)algorithm with chaotic optimization is proposed,which comprehensively considers the working indexes of time,energy consumption and pulsation impact.In the mathematical model of the working trajectory,the quintic NURBS curve is used to interpolate the end path points of the manipulator,the chaotic optimization method is integrated into the particle swarm optimization,and the dynamic weighting method is proposed to select the global optimal particle with the highest fitness;then the adaptive mutation rules of the particle swarm are improved through external archiving,so as to produce more excellent particles,reduce the possibility of local optimization and improve the operation efficiency,The Pareto value set space is improved.Through software simulation,under the known constraints,the optimization algorithm can realize the multi-objective optimization of the target trajectory and ensure that the welding manipulator runs in the optimal route.

关 键 词：多目标优化 自适应变异 五次NURBS曲线插值 混沌寻优 

分 类 号：TH165[机械工程—机械制造及自动化] TG65[金属学及工艺—金属切削加工及机床]