基于粒子群算法的Delta机器人正运动学的研究  

Forward Position Solution for the Delta Robot Based on Particle Swarm Optimization

作　　者：张进思 许棕 李泽彬 鲍惠芳 方杰 ZHANG Jinsi;XU Zong;LI Zebin;BAO Huifang;FANG Jie(School of Electrical and Photoelectronic Engineering,West Anhui University,Lu'an Anhui 237012,China)

机构地区：[1]皖西学院电气与光电工程学院,安徽六安237012

出　　处：《机械设计与研究》2023年第4期74-77,共4页Machine Design And Research

基　　金：国家自然科学基金资助项目(11905146);安徽省高等教育重点科研项目(KJ2020A0630);皖西学院科研启动资金项目(WGKQ2020010005)。

摘　　要：正运动学分析对Delta并联机器人轨迹规划和控制非常的重要。而Delta并联机器人的逆运动学求解比较容易,正运动学求解却相对困难。故研究利用基本的和带权重线性递减的粒子群算法应用于Delta并联机器人正运动学求解。通过改变不同的权重因子和学习因子,研究结果表明,这两种粒子群算法在一定的条件都能够快速及正确地对Delta并联机器人的正运动学求解。而且进一步研究数据也表明,带权重线性递减的改进版粒子群算法一定程度上要比基本粒子群算法更容易收敛于极值,收敛性更优。The analysis of the forward kinematics is very important for trajectory planning and control of a parallel robot.It is easy to obtain the inverse kinematics of the parallel robot,however the forward kinematics is difficult.This paper applies the particle swarm optimization with and without linearly decreasing weight for the analysis of the forward kinematics of the Delta robot.By changing different weight factors and learning factors,the results show that the two particle swarm optimization algorithms can quickly and correctly solve the forward kinematics of the Delta Robot under certain conditions.Moreover,the further research also shows that the improved particle swarm optimization algorithm with linearly decreasing weight is easier to converge to the extreme value and has better convergence than the basic particle swarm optimization algorithm.

关 键 词：正运动学 粒子群算法 Delta并联机器人 权重线性递减 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]