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Human - robot collision detection and identification based on fuzzy and time series modelling

  • Fotios Dimeas (a1), L. D. Avendaño-Valencia (a2) and Nikos Aspragathos (a1)

In this paper, two methods are proposed and implemented for collision detection between the robot and a human based on fuzzy identification and time series modelling. Both methods include a collision detection system for each joint of the robot that is trained to approximate the external torque. In addition, the proposed methods are able to detect the occurrence of a collision, the link that collided and to some extent the magnitude of the collision without using the explicit model of the robot. Since the speed of the detection is of critical importance for mitigating the danger, attention is paid to recognise a collision as soon as possible. Experimental results conducted with a KUKALWR manipulator using two joints in planar motion, verify the validity on both methods.

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