[1]ZHANG Yi,LIU Fangjun,HU Lei.A general robot inverse kinematics solution based on MPGA-RBFNN[J].CAAI Transactions on Intelligent Systems,2019,14(1):165-170.[doi:10.11992/tis.201805005]
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A general robot inverse kinematics solution based on MPGA-RBFNN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 1 Page number: 165-170 Column: 学术论文—智能系统 Public date: 2019-01-05
Title:
A general robot inverse kinematics solution based on MPGA-RBFNN
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
Keywords:
MPGA; RBFNN; general robot; inverse kinematics; hybrid coding; simultaneous evolutionary
CLC:
TP241.2
DOI:
10.11992/tis.201805005
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.
References:
[1] FARIA C, FERREIRA F, ERLHAGEN W, et al. Position-based kinematics for 7-DoF serial manipulators with global configuration control, joint limit and singularity avoidance[J]. Mechanism and machine theory, 2018, 121:317-334.
[2] 林阳, 赵欢, 丁汉. 基于多种群遗传算法的一般机器人逆运动学求解[J]. 机械工程学报, 2017, 53(3):1-8 LIN Yang, ZHAO Huan, DING Han. Solution of inverse kinematics for general robot manipulators based on multiple population genetic algorithm[J]. Journal of mechanical engineering, 2017, 53(3):1-8
[3] LIU Yuan, WANG Daqing, SUN Jian, et al. Geometric approach for inverse kinematics analysis of 6-Dof serial robot[C]//IEEE International Conference on Information and Automation. Lijiang, China:IEEE, 2015:852–855.
[4] ZANG Wenke, REN Liyan, ZHANG Wenqian, et al. A cloud model based DNA genetic algorithm for numerical optimization problems[J]. Future generation computer systems, 2018, 81:465-477.
[5] ALSHRAIDEH M A. Multiple-population genetic algorithm for solving min-max optimization problems[J]. International review on computers and software, 2015, 10(1):9-19.
[6] 林明, 王冠, 林永才. 改进的遗传算法在机器人逆解中的应用[J]. 江苏科技大学学报(自然科学版), 2012, 26(4):370-375 LIN Ming, WANG Guan, LIN Yongcai. Robot inverse kinematics based on improved genetic algorithm[J]. Journal of Jiangsu university of science and technology (natural science edition), 2012, 26(4):370-375
[7] AYYILDIZ M, ÇETINKAYA K. Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator[J]. Neural computing and applications, 2016, 27(4):825-836.
[8] STARKE S, HENDRICH N, MAGG S, et al. An efficient hybridization of Genetic Algorithms and Particle Swarm Optimization for inverse kinematics[C]//Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics. Qingdao, China:IEEE, 2017:1782–1789.
[9] HALALI M A, AZARI V, ARABLOO M, et al. Application of a radial basis function neural network to estimate pressure gradient in water-oil pipelines[J]. Journal of the Taiwan institute of chemical engineers, 2016, 58:189-202.
[10] CHIDDARWAR S S, BABU N R. Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach[J]. Engineering applications of artificial intelligence, 2010, 23(7):1083-1092.
[11] ZUBIZARRETA A, LARREA M, IRIGOYEN E, et al. Real time direct kinematic problem computation of the 3PRS robot using neural networks[J]. Neurocomputing, 2017, 271:104-114.
[12] TOSHANI H, FARROKHI M. Real-time inverse kinematics of redundant manipulators using neural networks and quadratic programming:a Lyapunov-based approach[J]. Robotics and autonomous systems, 2014, 62(6):766-781.
[13] KÖKER R. A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization[J]. Information sciences, 2013, 222:528-543.
[14] JIA Weikuan, ZHAO Dean, DING Ling. An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample[J]. Applied soft computing, 2016, 48:373-384.
[15] ZAPLANA I, BASANEZ L. A novel closed-form solution for the inverse kinematics of redundant manipulators through workspace analysis[J]. Mechanism and machine theory, 2018, 121:829-843.
[16] 赵建强, 刘满禄, 王姮. 基于PSO优化BP神经网络的逆运动学求解研究[J]. 自动化与仪表, 2016, 31(11):1-6 ZHAO Jianqiang, LIU Manlu, WANG Heng. Research on inverse kinematics solution of BP neural network based on PSO optimization[J]. Automation and instrumentation, 2016, 31(11):1-6
[17] 乔俊飞, 安茹, 韩红桂. 基于相对贡献指标的自组织RBF神经网络的设计[J]. 智能系统学报, 2018, 13(2):159-167 QIAO Junfei, AN Ru, HAN Honggui. Design of self-organizing RBF neural network based on relative contribution index[J]. CAAI transactions on intelligent systems, 2018, 13(2):159-167
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