[1]张毅,刘芳君,胡磊.结合MPGA-RBFNN的一般机器人逆运动学求解[J].智能系统学报,2019,14(01):165-170.[doi:10.11992/tis.201805005]
 ZHANG Yi,LIU Fangjun,HU Lei.A general robot inverse kinematics solution based on MPGA-RBFNN[J].CAAI Transactions on Intelligent Systems,2019,14(01):165-170.[doi:10.11992/tis.201805005]
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结合MPGA-RBFNN的一般机器人逆运动学求解(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年01期
页码:
165-170
栏目:
出版日期:
2019-01-05

文章信息/Info

Title:
A general robot inverse kinematics solution based on MPGA-RBFNN
作者:
张毅 刘芳君 胡磊
重庆邮电大学 重庆市信息无障碍与服务机器人工程技术研究中心, 重庆 400065
Author(s):
ZHANG Yi LIU Fangjun HU Lei
Chongqing Information Accessibility and Service Robot Technology Research Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
多种群遗传算法径向基函数神经网络一般机器人运动学逆解混合编码同时演化
Keywords:
MPGARBFNNgeneral robotinverse kinematicshybrid codingsimultaneous evolutionary
分类号:
TP241.2
DOI:
10.11992/tis.201805005
摘要:
针对一般机器人逆运动学求解过程中存在的求解速度慢、精度低的问题,将多种群遗传算法(multiple population genetic algorithm,MPGA)引入径向基函数神经网络(radial basis functions neural network,RBFNN),提出一种适用于一般机器人的高精度MPGA-RBFNN算法。该算法采用3层结构的RBFNN进行一般机器人逆运动学求解,结合一般机器人的正运动学模型,采用MPGA优化RBFNN的网络结构和连接权值的方法,同时应用混合编码和演化的方式,实现了从机器人工作空间位姿到关节角度的非线性映射,从而避免了复杂的公式推导并提高了求解速度。采用6R一般机器人作为实验平台进行实验,实验结果表明:MPGA-RBFNN算法不仅提高了一般机器人在逆运动学中的求解速度,而且MPGA-RBFNN算法的训练成功率和逆解的计算准确率也得到了提高。
Abstract:
In order to solve the problem of the inverse kinematics in a general robot, such as slow speed in problem-solving and lower solution accuracy, a high-precision algorithm is proposed for general robots, which introduces Multiple Population Genetic Algorithm into Radial Basis Functions neural network (MPGA-RBFNN). Combined with the positive kinematics model of general robots, a three-layer structure of RBFNN was used to solve the inverse kinematics, and the MPGA was adopted to optimize the network structure and connection weights of the RBFNN. By using hybrid coding and simultaneous evolutionary means, the non-linear mapping of the position of the robot in the working space to the joint angle was realized, avoiding complicated formula derivation and improving the speed of problem-solving. Finally, an experiment was conducted using the general 6R robot. The results showed that the speed of solving the problem of the inverse kinematics of a general robot was improved by the MPGA-RBFNN algorithm, and the training success rate of the MPGA-RBFNN algorithm and the calculation accuracy of the inverse kinematics were enhanced.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-05-07。
基金项目:国家自然科学基金项目(51775076,51604056).
作者简介:张毅,男,1966年生,教授,主要研究方向为智能机器人、数据融合、信息无障碍技术。主持国家、省部级以及产学研等各种项目10余项。发表学术论文100余篇,被SCI、EI和ISTP收录30余篇。出版著作5部;刘芳君,女,1990年生,硕士研究生,主要研究方向为工业机器人轨迹规划;胡磊,男,1994年生,硕士研究生,主要研究方向为工业机器人运动学控制。
通讯作者:刘芳君.E-mail:lfjyx163@163.com
更新日期/Last Update: 1900-01-01