Manipulator Collision Detection and Collided Link Identification Based on Neural Networks

  • Abdel-Nasser SharkawyEmail author
  • Panagiotis N. Koustoumpardis
  • Nikos A. Aspragathos
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 67)


In this paper, a multilayer neural network based approach is proposed for the human-robot collisions detection during the motions of a 2-DoF robot. One neural network is designed and trained by Levenberg-Marquardt algorithm to the coupled dynamics of the manipulator joints with and without external contacts to detect unwanted collisions of the human operator with the robot and the link that collided using only the proprietary joint position and joint torque sensors of the manipulator. The proposed method is evaluated experimentally with the KUKA LWR manipulator using two joints in planar horizontal motion and the results illustrate that the developed system is efficient and very fast in detecting the collisions as well as the collided link.


Collision detection Collided link identification Neural networks Proprietary sensors 



Abdel-Nasser Sharkawy is funded by the “Egyptian Cultural Affairs & Missions Sector” and “Hellenic Ministry of Foreign Affairs Scholarship” for Ph.D. study in Greece.


  1. 1.
    Mohammed, A., Schmidt, B., Wang, L.: Active collision avoidance for human – robot collaboration driven by vision sensors. Int. J. Com. Integr. Manuf. 30(9), 970–980 (2017)CrossRefGoogle Scholar
  2. 2.
    Flacco, F., Kroeger, T., De Luca, A., Khatib, O.: A depth space approach for evaluating distance to objects with application to human-robot collision avoidance. J. Intell. Robot. Syst. 80(Suppl 1), S7–S22 (2015)CrossRefGoogle Scholar
  3. 3.
    Lam, T.L., Yip, H.W., Qian, H., Xu, Y.: Collision avoidance of industrial robot arms using an invisible sensitive skin. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4542–4543 (2012)Google Scholar
  4. 4.
    Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G.: Collision detection and reaction: a contribution to safe physical human-robot interaction. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356–3363 (2008)Google Scholar
  5. 5.
    Cho, C., Kim, J., Lee, S., Song, J.: Collision detection and reaction on 7 DOF service robot arm using residual observer. J. Mech. Sci. Technol. 26(4), 1197–1203 (2012)CrossRefGoogle Scholar
  6. 6.
    Morinaga, S., Kosuge, K.: Collision detection system for manipulator based on adaptive impedance control law. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, pp. 1080–1085 (2003)Google Scholar
  7. 7.
    Dimeas, F., Avendano-valencia, L.D., Aspragathos, N.: Human - robot collision detection and identification based on fuzzy and time series modelling. Robotica, 1–13 (2014)Google Scholar
  8. 8.
    Lu, S., Chung, J.H., Velinsky, S.A.: Human-robot collision detection and identification based on wrist and base force/torque sensors. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 796–801, April 2005Google Scholar
  9. 9.
    Sharkawy, A.-N., Aspragathos, N.: Human-robot collision detection based on neural networks. Int. J. Mech. Eng. Robot. Res. 7(2), 150–157 (2018)CrossRefGoogle Scholar
  10. 10.
    Du, K., Swamy, M.N.S.: Neural Networks and Statistical Learning. Springer, London (2014)CrossRefGoogle Scholar
  11. 11.
    Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 2–6 (1994)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abdel-Nasser Sharkawy
    • 1
    • 2
    Email author
  • Panagiotis N. Koustoumpardis
    • 2
  • Nikos A. Aspragathos
    • 2
  1. 1.Mechanical Engineering Department, Faculty of EngineeringSouth Valley UniversityQenaEgypt
  2. 2.Department of Mechanical Engineering and AeronauticsUniversity of PatrasRioGreece

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