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Neural Network Design for Manipulator Collision Detection Based Only on the Joint Position Sensors

Published online by Cambridge University Press:  27 June 2019

Abdel-Nasser Sharkawy Open the ORCID record for Abdel-Nasser Sharkawy [Opens in a new window] ,
Panagiotis N. Koustoumpardis  and
Nikos Aspragathos
Abdel-Nasser Sharkawy*
Affiliation:
Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt Department of Mechanical Engineering and Aeronautics, University of Patras, Rio 26504, Greece. E-mails: koust@mech.upatras.gr, asprag@mech.upatras.gr
Panagiotis N. Koustoumpardis
Affiliation:
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio 26504, Greece. E-mails: koust@mech.upatras.gr, asprag@mech.upatras.gr
Nikos Aspragathos
Affiliation:
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio 26504, Greece. E-mails: koust@mech.upatras.gr, asprag@mech.upatras.gr
*
*Corresponding author. E-mail: eng.abdelnassersharkawy@gmail.com
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Summary

In this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link.

Keywords

Joints dynamicsRobot collision detectionCollided link identificationNeural networksIntrinsic sensors
Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1737 - 1755
Copyright
Copyright © Cambridge University Press 2019

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