Abstract
Human-robot collisions are unavoidable during a human-robot interaction. Thus, a number of collision detection algorithms have been proposed to ensure human safety during such an occasion. However, collision detection algorithms are usually model-based, requiring an accurate model of the robot. The errors in the model can lead to the malfunction of the algorithms. In this study, we propose an adaptation and collision detection scheme to improve the sensitivity of the collision detection algorithm. Performing adaptation prior to collision detection will effectively minimize the model uncertainty of the robot. This minimization will allow sensitive, reliable collision detection. By using torque filtering, adaptation and collision detection can be done without the need for the acceleration estimation. The performance of the proposed scheme is demonstrated by various experiments.
Original language | English |
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Title of host publication | 2013 9th Asian Control Conference, ASCC 2013 |
DOIs | |
Publication status | Published - 2013 Oct 31 |
Event | 2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey Duration: 2013 Jun 23 → 2013 Jun 26 |
Other
Other | 2013 9th Asian Control Conference, ASCC 2013 |
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Country | Turkey |
City | Istanbul |
Period | 13/6/23 → 13/6/26 |
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Keywords
- Adaptive control
- Collision detection
- Collision safety
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Adaptation-and-collision detection scheme for safe physical human-robot interaction. / Cho, Chang Nho; Kim, Young Loul; Song, Jae-Bok.
2013 9th Asian Control Conference, ASCC 2013. 2013. 6606028.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptation-and-collision detection scheme for safe physical human-robot interaction
AU - Cho, Chang Nho
AU - Kim, Young Loul
AU - Song, Jae-Bok
PY - 2013/10/31
Y1 - 2013/10/31
N2 - Human-robot collisions are unavoidable during a human-robot interaction. Thus, a number of collision detection algorithms have been proposed to ensure human safety during such an occasion. However, collision detection algorithms are usually model-based, requiring an accurate model of the robot. The errors in the model can lead to the malfunction of the algorithms. In this study, we propose an adaptation and collision detection scheme to improve the sensitivity of the collision detection algorithm. Performing adaptation prior to collision detection will effectively minimize the model uncertainty of the robot. This minimization will allow sensitive, reliable collision detection. By using torque filtering, adaptation and collision detection can be done without the need for the acceleration estimation. The performance of the proposed scheme is demonstrated by various experiments.
AB - Human-robot collisions are unavoidable during a human-robot interaction. Thus, a number of collision detection algorithms have been proposed to ensure human safety during such an occasion. However, collision detection algorithms are usually model-based, requiring an accurate model of the robot. The errors in the model can lead to the malfunction of the algorithms. In this study, we propose an adaptation and collision detection scheme to improve the sensitivity of the collision detection algorithm. Performing adaptation prior to collision detection will effectively minimize the model uncertainty of the robot. This minimization will allow sensitive, reliable collision detection. By using torque filtering, adaptation and collision detection can be done without the need for the acceleration estimation. The performance of the proposed scheme is demonstrated by various experiments.
KW - Adaptive control
KW - Collision detection
KW - Collision safety
UR - http://www.scopus.com/inward/record.url?scp=84886545526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886545526&partnerID=8YFLogxK
U2 - 10.1109/ASCC.2013.6606028
DO - 10.1109/ASCC.2013.6606028
M3 - Conference contribution
AN - SCOPUS:84886545526
SN - 9781467357692
BT - 2013 9th Asian Control Conference, ASCC 2013
ER -