Abstract
With the widespread use of service robots, safety issues regarding human-robot collisions have received increasing attention. The collision detection algorithm, which allows a robot to effectively detect and react against a collision, is considered as one of the most practical solutions for ensuring collision safety. However, these algorithms are often model-based, so it cannot ensure collision safety under payload variations or model uncertainty. In this paper, a novel collision detection algorithm based on torque filtering is proposed to cope with this problem. The torque due to the motion of the robot can be effectively removed using the Butterworth 2nd-order BPF (band pass filter) so that only the torque due to a collision is used for collision detection. This improves the robustness of the algorithm against model uncertainties. The proposed algorithm does not require the use of acceleration data. The performance of the algorithm was experimentally verified.
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Recommended by Editorial Board member Youngjin Choi under the direction of Editor Hyouk Ryeol Choi.
This work was supported by the Human Resources Development Program for Convergence Robot Specialists and by Korea University Research Fund.
Chang-Nho Cho received his B.S. degree in Applied Science from the University of British Columbia, Vancouver, Canada, in 2010. He received his M.S degree in Mechanical Engineering from Korea University. His research interests include safe human-robot interactions and human-robot cooperation.
Jae-Bok Song received his B.S. and M.S. degrees in Mechanical Engineering from Seoul National University, in 1983 and 1985, respectively, and his Ph.D. degree in Mechanical Engineering from MIT in 1992. He joined the faculty of Korea University in 1993. His current research interests include design and control of robot arms and navigation of a mobile robot.
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ho, CN., Song, JB. Collision detection algorithm robust to model uncertainty. Int. J. Control Autom. Syst. 11, 776–781 (2013). https://doi.org/10.1007/s12555-012-0235-6
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DOI: https://doi.org/10.1007/s12555-012-0235-6