A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators

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Santosh Kumar Nanda 1,* Swetalina Panda 1 P Raj Sekhar Subudhi 1 Ranjan Kumar Das 1

1. Department of Computer Science and Engineering, Eastern Academy of Science and Technology, Bhubaneswar, Odisha, India – 754001

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.09.11

Received: 22 Sep. 2011 / Revised: 11 Jan. 2012 / Accepted: 19 Mar. 2012 / Published: 8 Aug. 2012

Index Terms

Inverse Kinematics, Artificial Intelligence, Multilayer Perceptron, Functional Link Artificial Neural Network


In robotic applications and research, inverse kinematics is one of the most important problems in terms of robot kinematics and control. Consequently, finding the solution of Inverse Kinematics in now days is considered as one of the most important problems in robot kinematics and control. As the intricacy of robot manipulator increases, obtaining the mathematical, statistical solutions of inverse kinematics are difficult and computationally expensive. For that reason, now soft-computing based highly intelligent based model applications should be adopted to getting appropriate solution for inverse kinematics. In this paper, a novel application of artificial neural network is used for controlling a robotic manipulator. The proposed methods are based on the establishments of the non-linear mapping between Cartesian and joint coordinates using multi layer perceptron and functional link artificial neural network.

Cite This Paper

Santosh Kumar Nanda, Swetalina Panda, P Raj Sekhar Subudhi, Ranjan Kumar Das, "A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.9, pp.81-91, 2012. DOI:10.5815/ijisa.2012.09.11


[1]Robert J. Schilling, Fundamentals of Robotics: Analysis and Control, Prentice Hall, New Jersey, 1990.

[2]John J. Craig, Introduction to Robotics: Mechanics and Control, Prentice Hall, New Jersey, 2004.

[3]Santosh Kumar Nanda, Ashok Kumar Dash, Sandigdha Acharya, Abikshyana Moharana. Application of Robotics in Mining Industry: A Critical Review, Mining Technology-Extraction, Beneficiation for Safe & Sustainable Development, Indian Mining & Engg. Journal, Mine TECH’10, pp.108-112, 2010.

[4]Christian Smith, Henrik I. Christensen. Robot Manipulator, IEEE Robot and Automation Magazine, vol. 64, 2009, pp 75-83.

[5]A. S. Morris, A. Mansor. Finding the Inverse Kinematics of Manipulator Arm using Artificial Neural Network with Lookup Table, Robotica, vol. 15,1997, pp. 617–625.

[6]S Alavandar, M.J. Nigam. Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators, Int. J. of Computers, Communications & Control, vol. 3, 2008, pp 224-234.

[7]Jolly Shah, S.S. Rattan, B.C. Nakra. Kinematics Analysis of 2-DOF Planar Robot Using Artificial Neural Network”, World Academy of Science, Engineering and Technology, vol. 81, 2011, pp. 282-285. 

[8]Simon Haykin. Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998

[9]Madan Gupta, Liang. M, Jin, Noriyasu Homma. Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory, Wiley-IEEE Press, USA, 2003.

[10]Allon Guez, James Ellbert, M. L. Kam. Neural networks architecture for control, IEEE Control System magazine, 1998, pp.22-25.

[11]Y. –H Pao. Adaptive pattern recognition and neural networks, Addison-Wesley, Reading, Massachusetts, 1989

[12]S.K. Nanda, D.P. Tripathy, S.K. Patra. Application of Soft Computing for noise prediction model for opencast mines, Noise Control Engineering Journal, USA, vol.59, issue 5, 2011, pp. 432-446.

[13]S.K. Nanda, D.P. Tripathy. Application of Functional Link Artificial Neural Network for Noise Prediction in Mining Industry, International Journal of Advances of Fuzzy Logic System, USA. http:// dx.doi.org/10.1155/2011/831261 

[14]S.K. Nanda, D.P. Tripathy. Application of Legendre Neural Network for Air Quality Prediction, ICET-2011, The 5th PSU-UNS International Conference on Engineering and Technology (ICET2011), Phuket, May 2-3, Malaysia, 2011, pp. 267-272.

[15]J. C. Patra, R. N. Pal. Functional link artificial neural network-based adaptive channel equalization of nonlinear channels with QAM signal,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 3, 1995, pp.2081–2086.

[16]J. C. Patra, R. N. Pal, R. Baliarsingh, G. Panda. Nonlinear channel equalization for QAM signal constellation using artificial neural networks, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol.29 issue 2, 1999, pp. 262–271.