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Real-Time Neural-Net Driven Optimized Inverse Kinematics for a Robotic Manipulator Arm

  • Jui ShahEmail author
  • Arjun Bhasin
Conference paper
  • 62 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

this paper proposes a method that optimizes the inverse kinematics needed for the trajectory generation of a 4-dof (degrees of freedom) robotic manipulator arm to give results in real time. due to the many-to-one mapping of the angle vector which describes the position of the manipulator joints and to the coordinates of the end-effector, traditional methods fail to address the redundancy that exists in an efficient way. the proposed method is singular, and in that it (1) generates the most optimal angle vector in terms of maximum manipulability, a factor which determines the ease with which the arm moves, for a given end-vector. (2) proposes a novel approach to inculcate the complexity of dealing with real coordinate system by proposing a machine learning technique that uses neural networks to predict angle vector for practically any end-effector position although it learns on only a few sampled space. (3) works in real time since the entire optimization of the manipulability measure are done offline before training the neural network using a relevant technique which makes the proposed method suitable for practical uses. (4) it also determines the shortest, smooth path along which the manipulator can move along avoiding any obstacles. to the best of the authors’ knowledge, this is the first neural-net-based optimized inverse kinematics method applied for a robotic manipulator arm, and its optimal and simple structure also makes it possible to run it on nvidia jetson nano module.

Keywords

Inverse kinematics Neural networks Robotic manipulator arm Manipulability Redundancy Degree of freedom Optimization 

References

  1. 1.
    Manipulator (device).
  2. 2.
    Kaur, K.: Basic robotics: what is a robotic manipulator? (2013).
  3. 3.
    White papers-redundant system basic concepts, national instruments (2008).
  4. 4.
    Nakamura, Y.: Advanced Robotics: Redundancy and Optimization. Addison-Wesley (1991)
  5. 5.
    Nakamura, Y., Hanafusa, H.: Optimal redundancy control of robot manipulators. Int. J. Robot. Res. (1987)
  6. 6.
    Yoshikawa, T.: Foundation of Robotics: Analysis and Control. MIT Press (1990)
  7. 7.
    Yoehikawa, T.: Translational and rotational manipulability of robotic manipulators. In: American Control Conference (1990)
  8. 8.
    Yoehikawa, T.: Dynamic manipulability of robotic manipulators. In: IEEE International Conference on Robotics and Automation (1985)
  9. 9.
    Kurfess, T.R.: Robotics and Automation Handbook. CRC Press LLC (2000)
  10. 10.
    Buss, S.R.: Introduction to inverse kinematics with Jacobian transpose pseudoinverse and damped least squares methods (2004)
  11. 11.
    Duka, A.-V.: Neural network based inverse kinematics solution for trajectory tracking of a robotic arm (2013)
  12. 12.
    Dinh, B.H.: Approximation of the inverse kinematics of a robotic manipulator using a neural network (2009)
  13. 13.
    Murray, R.M.: A Mathematical Introduction to Robotic Manipulation. CRC Press (2017)
  14. 14.
    Serrezuela, R.R., Chavarro, A.F., Cardozo, M.A.T., Toquica, A.L., Martinez, L.F.O.: Kinematic modelling of a robotic arm manipulator using Matlab. ARPN J. Eng. Appl. Sci. (2017)
  15. 15.
    Tejomurtula, S., Kak, S.: Inverse kinematics in robotics using neural networks. Information Sciences (1998)
  16. 16.
    Srisuk, P., Sento, A., Kitjaidure, Y.: Inverse kinematics solution using neural networks from forward kinematics equations. In: 9th International Conference on Knowledge and Smart Technology (KST) (2017)
  17. 17.
    Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.: STOMP: stochastic trajectory optimization for motion planning (2011)
  18. 18.
    Martinez, J.: Manipulability ellipsoids in robotics
  19. 19.
    Rozo, L., Jaquier, N., Calinon, S., Caldwell, D.G.: Learning manipulability ellipsoids for task compatibility in robot manipulation. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Dhirubhai Ambani Institute of Information and Communication TechnologyGandhinagarIndia
  2. 2.Fero DWC-LLCDubaiUnited Arab Emirates

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