移动焊接机械臂自适应粒子群逆运动学求解算法

Adaptive Particle Swarm Optimization Algorithm for Solving Inverse Kinematics of Mobile Welding Robot

针对具有冗余自由度的移动焊接机械臂逆运动学求解复杂、精度低等问题,提出一种自适应粒子群算法(APSO)进行求解。首先,采用DH参数法建立机械臂的运动学模型,以位姿误差最小为目标,结合运动过程中关节行程最小原则建立了移动机械臂逆运动学求解优化模型;其次,APSO算法根据粒子到个体最优位置和全局最优位置的距离,采用基于双曲正切函数的策略自适应调整加速度系数,以改善解的精度和收敛速度,提升算法综合性能;最后,与标准粒子群算法(PSO)、基于时变加速度系数的粒子群算法(PSO-TVAC)进行对比试验,通过对单点测试和圆弧轨迹规划的仿真分析,结果表明APSO算法收敛速度快、求解精度高,能有效提高移动焊接机械臂逆运动学解的精度和误差稳定性。

An adaptive particle swarm optimization(APSO)algorithm was proposed to solve the complex and low accuracy inverse kinematics of mobile welding manipulator with redundant degrees of freedom.Firstly,the DH parameter method was used to establish the kinematics model of the manipulator,and the inverse kinematics optimization model of the mobile manipulator was established with the objective of minimizing the pose error and the principle of minimizing the joint stroke in the process of motion;Secondly,APSO algorithm adaptively adjusts the acceleration coefficient based on the strategy of hyperbolic tangent function according to the distance from the particle to the individual optimal position and the global optimal position,so as to improve the accuracy and convergence speed of the solution and improve the comprehensive performance of the algorithm;Finally,compared with standard particle swarm optimization(PSO)and particle swarm optimization algorithm based on time-varying acceleration coefficient(PSO-TVAC),through the simulation analysis of single point test and arc trajectory planning,the results show that APSO algorithm has fast convergence speed and high accuracy,and can effectively improve the accuracy and error stability of inverse kinematics solution of mobile welding manipulator.