机械科学与技术2024，Vol.43Issue(3)：423-429,7.DOI:10.13433/j.cnki.1003-8728.20220271
改进粒子群算法的自动充电机械臂时间最优轨迹研究
Exploring Time-optimal Trajectory of Automatic Charging Manipulator with Improved Particle Swarm Optimization Algorithm
摘要
Abstract
A particle swarm optimization(PSO)algorithm based on the nonlinear dynamic learning factor was proposed to solve the time optimization problem in the joint space trajectory planning of a truss charging manipulator.The workspace was obtained through kinematic analysis,and the 3-5-3 polynomial interpolation was introduced for the trajectory planning.The shortest motion time was sought through combining velocity constraints with acceleration constraints.The convergence speed of the improved PSO algorithm was compared with that of the basic PSO algorithm,and the variation of motion time of each joint before and after optimization was analyzed.The simulation results show that the convergence performance of the improved PSO algorithm is faster than that of the basic PSO algorithm and that the overall motion time is shortened by about 33%,confirming the feasibility of the improved PSO algorithm.关键词
桁架充电机械臂/时间优化/非线性动态学习因子/粒子群算法Key words
truss charging manipulator/time optimization/nonlinear dynamic learning factor/particle swarm optimization algorithm分类
机械制造引用本文复制引用
朱浩,赵清海,郑群锋,宁长久..改进粒子群算法的自动充电机械臂时间最优轨迹研究[J].机械科学与技术,2024,43(3):423-429,7.基金项目
国家自然科学基金项目(52175236) （52175236）
