粒子群优化
克里金
机器人
运动学
工业机器人
替代模型
计算机科学
鉴定(生物学)
流离失所(心理学)
惯性
趋同(经济学)
工程类
多群优化
数学优化
人工智能
算法
控制理论(社会学)
机器学习
数学
物理
经济增长
植物
经典力学
经济
心理治疗师
控制(管理)
心理学
生物
作者
Wanghao Shen,Guojun Liu,Jialong He,Guofa Li,Liangsheng Han
摘要
Abstract To tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit‐Hartenberg and the small displacement screw methods, the kinematic model of a six‐degree‐of‐freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub‐population cooperation under the multi‐attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.
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