Inverse Kinematic Analysis of 5-DOF and 7-DOF Redundant Manipulator using ANFIS

Authors

  • Mohit Kumar  M. Tech, Department of Mechanical Engineering, Millennium Institute of Technology, Bhopal, Madhya Pradesh, India
  • B. S. Choudhary  Faculty, Department of Mechanical Engineering, Millennium Institute of Technology, Bhopal, Madhya Pradesh, India

Keywords:

Robots, Redundant Manipulator, ANFIS, inverse kinematics

Abstract

Robots are one of the prominent aspects of current technological advancements. Current research study focuses on conducting Inverse Kinematic Analysis of 5-DOF & 7-DOF Redundant Manipulator using ANFIS. ANFIS stands for Adaptive Neuro-fuzzy Inference system. In this study ANFIS modelling has been performed to solve complex, nonlinear and discontinuous kinematics equation for a complex robot manipulator. It is also desired to find an ANFIS approach that provides a general frame work for combination of NN and fuzzy logic.The difference in joint angle deduced and predicted with ANFIS model for a 5-DOF and 7-DOF Redundant manipulator clearly depicts that the proposed method results with an acceptable error. Predicted (training data) values range from (-0.01 to 0.01) for both 5-DOF & 7- DOF and finding values (testing data) are range from (-0.03 to 0.03) it means the value decrease is about 0.02. The methods used for deriving the inverse kinematics model for these manipulators could be applied to other types of robotic arms, such as the EduBots developed by the Robotica Ltd, Pioneer 2 robotic arm (P2Arm), 5-DOF Lynx 6 Educational Robot arm. It can be concluded that the solution developed in this paper will make the PArm more useful in application with unpredicted trajectory movement in unknown environment.

References

  1. Deb S.R. Robotics technology and flexible automation, Tata McGrow-Hill Publishing Company Limited. New-Delhi, 2008.
  2. Clarke, R. Asimov’s Laws of Robotics: Implications for Information Technology- part II, Computer, 27(1), September (1994): pp.57–66.
  3. Martin, F.G. Robotic Explorations: A Hands-On Introduction to Engineering, Prentice Hall, New Jersey, 2001.
  4. Featherstone R. Position and velocity transformations between robot end-effector coordinates and joint angles. International journal of Robotic Research, SAGE Publications, 2(2), (1983), pp. 35-45.
  5. Nieminen and Peetu, et al. Water hydraulic manipulator for fail safe and fault tolerant remote handling operations at ITER, Fusion Engineering and Design, Elsevier, Vol. 84, (2009), pp. 1420-1424.
  6. Hollerbach J.M. Optimum kinematic design for seven degree of freedom manipulator, Second International Symposium on Robotics Research. Cambridge: MIT Press, (1985): pp. 215-222.
  7. Sciavicco L. and Siciliano B. Modelling and Control of Robot Manipulators, Springer Second edition, (2000), Chapter 3, pp. 96.
  8. Shimizu M., Kakuya H. Yoon W, Kitagaki K., and Kosuge K., Analytical Inverse Kinematic Computation for 7-DOF Redundant Manipulators with Joint Limits and its application to Redundancy. Resolution, IEEE Transaction on Robotics, October (2008), 24(5).
  9. Vassilopoulos A.P and Bedi R. Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates, Computation and Material Science.43 (2008): pp. 1086-1093.
  10. Haykin S., Neural Networks- A Comprehensive Foundation, McMillan College Publishing, New York, 1998.
  11. Mendel J.M. Fuzzy logic system for engineering: A tutorial, Proceeding, IEEE. 83(3) (1995): pp. 345-377.
  12. Ke L., Hong-ge M., Hai-jing Z. Application of Adaptive Neuro-Fuzzy Inference System to Forecast of Microwave Effect, IEEE Conference Publications, (2009) , pp. 1- 3.
  13. Alavandar S. and Nigam M. J. Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator, Journal of Engineering Science and Technology Review,1 (2008): pp.106- 111.
  14. Craig J.J. Introduction to Robotics: Mechanisms and Controls, Addison-Wesley, Reading, MA, 1989.
  15. Lee G.C.S. Dynamics and Control, Robot Arm Kinematics, Computer, 15(1982), Issue.12: pp. 62-79.
  16. Korein J.U and Balder N.I. Techniques for generating the goal-directed motion of articulated structures’, Institute of Electrical and Electronics Engineers Computer Graphics Applications, 2(1982), Issue. 9: pp. 71-81.
  17. Srinivasan A and Nigam M.J. ‘Neuro-Fuzzy based Approach for Inverse Kinematics Solution of Industrial Robot Manipulators’, International Journal of Computers, Communications and Control, III (2008), No. 3: pp. 224-234.
  18. Calderon C.A.A., Alfaro E.M.R.P, Gan J.Q. and Hu H. Trajectory generation and tracking of a 5-DOF Robotic Arm. CONTROL, University of Bath, (2004).
  19. De X., Calderon C.A.A., Gan J.Q., H Hu. An Analysis of the Inverse Kinematics for a 5-DOF Manipulator, International Journal of Automation and Computing, (2) (2005): pp. 114-124.
  20. Gan J.Q., Oyama E., Rosales E.M. and Hu, H. A complete analytical solution to the inverse kinematics of the Pioneer 2 robotic arm, Robotica, Cambridge University Press. 23(2005): pp. 123–129.
  21. Hasan A.T. and Al-Assadi H.M.A.A. Performance Prediction Network for Serial Manipulators Inverse Kinematics Solution Passing Through Singular Configurations, International Journal of Advanced Robotic Systems, 7(2010), No. 4: pp. 10-23.

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Published

2023-04-30

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Section

Research Articles

How to Cite

[1]
Mohit Kumar, B. S. Choudhary, " Inverse Kinematic Analysis of 5-DOF and 7-DOF Redundant Manipulator using ANFIS , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.97-127, March-April-2023.