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

Published online by Cambridge University Press:  09 May 2014

Fotios Dimeas ,
L. D. Avendaño-Valencia  and
Nikos Aspragathos
Fotios Dimeas*
Affiliation:
Department of Mechanical Engineering & Aeronautics, Robotics Group, University of Patras, Greece
L. D. Avendaño-Valencia
Affiliation:
Department of Mechanical Engineering & Aeronautics, Stochastic Mechanical Systems & Automation Lab, University of Patras, Greece
Nikos Aspragathos
Affiliation:
Department of Mechanical Engineering & Aeronautics, Robotics Group, University of Patras, Greece
*
*Corresponding author. E-mail: dimeasf@mech.upatras.gr
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Summary

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.

Keywords

Robot collision detectionSafe human-robot interactionFuzzy modellingTime series modelling

Information

Type
Articles
Information
Robotica , Volume 33 , Issue 9 , November 2015 , pp. 1886 - 1898
Copyright
Copyright © Cambridge University Press 2014 

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