Abstract
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.
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Acknowledgments
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.
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Sharkawy, AN., Koustoumpardis, P.N., Aspragathos, N.A. (2019). Manipulator Collision Detection and Collided Link Identification Based on Neural Networks. In: Aspragathos, N., Koustoumpardis, P., Moulianitis, V. (eds) Advances in Service and Industrial Robotics. RAAD 2018. Mechanisms and Machine Science, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-030-00232-9_1
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DOI: https://doi.org/10.1007/978-3-030-00232-9_1
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