Inverse Kinematics Solution for Robot Manipulator based on Neural Network under Joint Subspace
Keywords:
Inverse kinematics, neural network, extreme learning machineAbstract
Neural networks with their inherent learning ability have been widely applied to solve the robot manipulator inverse kinematics problems. However, there are still two open problems: (1) without knowing inverse kinematic expressions, these solutions have the difficulty of how to collect training sets, and (2) the gradient-based learning algorithms can cause a very slow training process, especially for a complex configuration, or a large set of training data. Unlike these traditional implementations, the proposed metho trains neural network in joint subspace which can be easily calculated with electromagnetism-like method. The kinematics equation and its inverse are one-to-one mapping within the subspace. Thus the constrained training sets can be easily collected by forward kinematics relations. For issue 2, this paper uses a novel learning algorithm called extreme learning machine (ELM) which randomly choose the input weights and analytically determines the output weights of the single hidden layer feedforward neural networks (SLFNs). In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The results show that the proposed approach has not only greatly reduced the computation time but also improved the precision.
References
Bruno Siciliano, Oussama Khatib, Springer Handbook of robotics,Springer Press,2008. http://dx.doi.org/10.1007/978-3-540-30301-5
H JACK, DMA LEE, RO BUCHAL and WH ELMARAGHY, Neural networks and the inverse kinematics problem, Journal of intelligent manufacturing,4:43-66,2003.
FL Lewis,Neural network control of robot manipulators,IEEE Expert,11(3):64-75,1996. http://dx.doi.org/10.1109/64.506755
BB Choi and C Lawrence,Inverse kinematics problem in robotics using neural networks,NASA Technical Memorandum-105869.
Z Binggul, HM Ertunc and C Oysu, Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset,In Proceedings of the 2005 Congress on Computational Intelligence Method and Application,pp:1-5.
AS Morris, A Mansor,Finding the inverse kinematics of manipulator arm using artificial neural network with look-up table. Robotica,15:617-625,1997. http://dx.doi.org/10.1017/S026357479700074X
JA Driscoll,Comparison of neural network architectures for the modeling of robot inverse kinematics, In Proceedings of the 2000 IEEE,3:44-51,2000.
SS Yang, M Moghavvemi and John D Tolman,Modelling of robot inverse kinematics using two ANN paradigms, In Proceedings of TENCON2000 Intelligent System and Technologies for the New Millennium,3:173-177,2000.
Shital S, Chiddarwar N and Ramesh Babu,Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach,Engineering Applications of Artificial Intelligence, 23(7):1083-1092,2010. http://dx.doi.org/10.1016/j.engappai.2010.01.028
PY Zhang, TS Lu and LB Song, RBF neworks-based inverse kinematics of 6R manipulator,Int. Journal of advanced manufacturing technology,26:144-147,2004. http://dx.doi.org/10.1007/s00170-003-1988-0
Eimei Oyama, Arvin Agah and Karl F, A modular neural architecture for inverse kinematics model learning,Neurocomputing, 38(40):797-805,2001. http://dx.doi.org/10.1016/S0925-2312(01)00416-7
Srinivasan Alavandar, MJ Nigam, Neuro-Fuzzy based approach for inverse kinematics solution of industrial robot manipulators, Int. J. of computers, Communication and Control, 3(3):224-234,2008.
Karlra P, Prakash NR, A neuro-genetic algorithm approach for solving inverse kinematics of robotic manipulators, IEEE International Conference on Systems, Man and Cybernetics,2:1979-1984,2003.
Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew, Extreme learning machine: A new learning scheme of feedforward neural networks, In Proceedings of IEEE International joint conference on Neural Networks,2:985-990,2004.
Guang-Bin Huang, Lei Chen, Enhanced random search based incremental extreme learning machine, Neurocomputing, 71(16-18):3460-3468,2008. http://dx.doi.org/10.1016/j.neucom.2007.10.008
Birbil SI, Fang SC, An electromagnetism-like mechanism for global optimization,Journal of Global Optimization, 23(3):263-282,2003. http://dx.doi.org/10.1023/A:1022452626305
Birbil SI, Fang SC, Sheu RL, On the convergence of a population-based global optimization algorithm, Journal of global optimization, 30:301-318,2004. http://dx.doi.org/10.1007/s10898-004-8270-3
Wang LCT, Chen CC, A combined optimization method for solving the inverse kinematics problem of mechanical manipulators,IEEE Transaction on Robotics and Automation, 7(4):489-499,1991. http://dx.doi.org/10.1109/70.86079
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.