基于粒子群优化算法的焊接机械臂轨迹规划

Trajectory Planning of Welding Manipulator Based on Particle Swarm Optimization Algorithm

针对焊接机械臂轨迹规划问题,提出了一种改变传统粒子群优化算法中学习因子的方法。传统粒子群算法的粒子学习因子在迭代过程中取0到1之间的随机数,这极大地影响算法的计算速度。本文首先按照焊接机械臂的作业背景构造函数,使学习因子随着迭代次数有序取数,极大地提高了算法计算速度,从而减少了机械臂的轨迹规划和运动时间。其次构建焊接机械臂模型,搭建机械臂的D-H参数轴和各杆件坐标系,并建立机械臂的运动学方程。最后通过MATLAB进行轨迹规划和运动学仿真得到了改进前后的机械臂收敛速度对比图。结果表明:传统粒子群算法的寻路时间约为9.0 s,而改进后的粒子群算法寻路时间约为2.1 s,优化时间为6.9 s,机械臂寻找目标物的效率大大提高。本研究对于提高焊接机械臂轨迹规划的效率和精度具有重要的意义。

Aiming at the trajectory planning problem of welding manipulator,a method to change the learning factor in the traditional particle swarm optimization algorithm is proposed.The particle learning factor of the traditional particle swarm algorithm takes a random number between 0 and 1 in the iterative process,which greatly affects the calculation speed of the algorithm.This article firstly construct the function according to the operational background of the welding robotic arm,so that the learning factors are sequentially taken with the number of iterations,greatly improving the algorithm's calculation speed and reducing the trajectory planning and motion time of the robot arm.Secondly,the model of the welding manipulator is constructed,the D-H parameter axis of the manipulator and the coordinate system of each member are constructed,and the kinematic equation of the manipulator is established.Finally,the trajectory planning and kinematic simulation are carried out by MATLAB,and the comparison and convergence velocity diagram of the manipulator before and after the improvement are obtained.The results show that the pathfinding time of the traditional particle swarm algorithm is about 9.0 s,while the pathfinding time of the improved particle swarm algorithm is about 2.1 s and the optimization time is 6.9 s,so that the efficiency of the robotic arm to find the target is greatly improved.This study is of great significance for improving the efficiency and accuracy of trajectory planning of welding manipulators.