摘要
为提升并联机器人的定位精度,确保在执行各种任务时能够达到更高的精度要求,设计了一种基于天牛须粒子群(BAS-PSO)算法的并联机器人定位误差补偿方法,以克服现有方法存在的定位精度较低的问题。基于改进型丹纳维特-哈滕伯格(MDH)模型构建并联机器人定位误差模型,以改善丹纳维特-哈滕伯格(DH)模型在实际应用中的局限性。采用BAS-PSO算法对MDH定位误差模型的误差参数展开辨识,求解误差未知量。利用并联机器人的实际定位坐标对辨识结果实施基于空间插值的神经网络定位误差补偿。实验测试结果表明,所提设计方法定位误差补偿后准确度最高达到98.05%,且定位误差得到大幅降低。研究结果不仅为并联机器人精度补偿提供了新思路,所提出的混合优化算法和误差建模方法对其他精密装备的精度提升也具有重要参考价值。
To improve the positioning accuracy of parallel robots and ensure that they can achieve higher accuracy requirements when performing various tasks,a compensation method for positioning error of parallel robots based on the beetle antennae search-particle swarm optimization(BAS-PSO)algorithm was designed to overcome the problem of low positioning accuracy in existing methods.Based on modified Denavit-Hartenberg(MDH),a parallel robot positioning error model was constructed to improve the limitations of the conventional Denavit-Hartenberg(DH)model in practical applications.The BAS-PSO algorithm was employed to identify the error parameters in the MDH-based positioning error model,thereby solving for the unknown errors.Subsequently,the actual positioning coordinates of parallel robots were used to implement neural network positioning error compensation based on spatial interpolation for identification results.The experimental results demonstrate that the highest accuracy value of the proposed design method after positioning error compensation reaches 98.05%,and the positioning error has been significantly reduced.The research results not only provide a new idea for the accuracy compensation of parallel robots,but also have important reference value for improving the accuracy of other precision equipment through the proposed hybrid optimization algorithm and error modeling method.
作者
孙晓宁
SUN Xiaoning(Business School,Zhumadian Vocational and Technical College,Zhumadian 463000,China)
出处
《成都工业学院学报》
2025年第4期24-28,60,共6页
Journal of Chengdu Technological University
基金
河南省科技攻关计划项目(222102240106)。
关键词
改进型丹纳维特-哈滕伯格模型
天牛须粒子群算法
并联机器人
神经网络
空间插值
定位误差补偿
modified Denavit-Hartenberg(MDH)model
beetle antennae search-particle swarm optimization(BAS-PSO)algorithm
Parallel Robot
Neural Network
Spatial Interpolation
positioning error compensation
作者简介
孙晓宁(1995-),男,助教,硕士,研究方向为计算机科学与技术。电子邮箱:asdfg0012515@163.com。
