摘要
为了在提高串联机械臂工作效率的同时保证运行轨迹良好的节能性、平稳性及灵活性,设计新的多目标轨迹优化方法。采用高次NURBS(non-uniform rational B-Spline,NURBS)曲线插值方法,构造机械臂高阶连续、首尾运动参数均可指定且具有局部修改特性的关节轨迹,保证了机械臂的运动性能。采用改进多目标灰狼算法(improved multi-objective grey wolf optimizer,IMOGWO)对机械臂轨迹进行优化,得到Pareto最优解集,该算法采用SIN混沌初始种群、非线性收敛因子、个体维度位置学习及领导者广义反向学习变异,并通过不可行度来处理多物理约束条件。在六自由度串联机械臂上的仿真实验结果表明,采用高次NURBS曲线规划方法可得到高阶连续的分段可调轨迹,采用IMOGWO算法可对NURBS曲线实现有效的多目标寻优,得到较为理想的Pareto分布,为用户提供多样化选择方案。
In order to improve the working efficiency of the series robotic arm while ensuring good energy saving,smoothness and flexibility of the operating trajectory,a new multi-objective trajectory optimization method is designed.The high-degree NURBS(non-uniform rational B-Spline,NURBS)was adopted to construct a continuous path with controllable start-stop kinematic parameters and locally adjustable which guaranteed the motion performance of manipulators.The IMOGWO(improved multi-objective grey wolf optimizer,IMOGWO)algorithm was applied to optimize the trajectory of manipulators in order to get a set of Pareto optimal solution aggregate.The algorithm used chaos model to generate the initial populations,nonlinear convergence factor,Individual dimensional positional learning,leader generalized reverse learning mutation and infeasibility degree selection to handle the constraints.The results on a six-degree of freedom serial robot manipulator show that high-degree NURBS can get high-degree continuous trajectories and locally adjustable.IMOGWO provides an effective approach to do a multi-objective optimal for NURBS and can obtain good,distributed Pareto solutions providing more choices for users.
作者
田国富
张毅
TIAN Guofu;ZHANG Yi(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110000,China)
出处
《组合机床与自动化加工技术》
北大核心
2024年第9期15-19,24,共6页
Modular Machine Tool & Automatic Manufacturing Technique
基金
辽宁省教育厅项目(LFGD2020006)。
