高技术通讯2025，Vol.35Issue(10)：1133-1144,12.DOI:10.3772/j.issn.1002-0470.2025.10.010
基于PSO-RBF神经网络的智能钻机机械臂误差研究
Research on the error of the manipulator of an intelligent drilling rig based on PSO-RBF neural network
摘要
Abstract
With the continuous growth of the intelligent requirements for infrastructure construction and mining,intelli-gent drilling rigs have become key equipment for improving the efficiency and accuracy of tunnel construction.Fo-cusing on the kinematic error compensation technology of the manipulator of an intelligent drilling rig,aiming at its requirements for high-precision positioning and automated construction,this paper proposes a systematic solution and conducts in-depth research from theoretical analysis to experimental verification.It explores the sources of manipula-tor errors and their influence on the end-positioning accuracy,and derives the kinematic parameter error model using the differential transformation theory.In terms of error compensation methods,a radial basis function(RBF)neural network model optimized by the particle swarm optimization(PSO)algorithm is proposed.Compared with traditional parameter compensation methods,PSO-RBF neural network can approximate the non-linear error model more effi-ciently and significantly improve the compensation accuracy and robustness.Experimental results show that this method can reduce the average end-positioning error of the manipulator by 93.66％,verifying its potential for com-pensating errors in complex manipulators.Using a dual-arm intelligent drilling rig as the test platform,by comparing the parameter error compensation method and the PSO-RBF neural network compensation method,the superiority and applicability of the proposed intelligent algorithm are further proven.关键词
智能钻机/机械臂/误差补偿Key words
intelligent drilling rig/manipulator/error compensation引用本文复制引用
陈继府,伍鼎灿,谢习华..基于PSO-RBF神经网络的智能钻机机械臂误差研究[J].高技术通讯,2025,35(10):1133-1144,12.基金项目
湖南省自然科学基金(2024ZYC020)资助项目. （2024ZYC020）
