Details
| Original language | English |
|---|---|
| Title of host publication | 2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025 |
| Publisher | IEEE Computer Society |
| Pages | 2562-2567 |
| Number of pages | 6 |
| ISBN (electronic) | 9798331522469 |
| ISBN (print) | 979-8-3315-2247-6 |
| Publication status | Published - 17 Aug 2025 |
| Event | 21st IEEE International Conference on Automation Science and Engineering, CASE 2025 - Los Angeles, United States Duration: 17 Aug 2025 → 21 Aug 2025 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| ISSN (Print) | 2161-8070 |
| ISSN (electronic) | 2161-8089 |
Abstract
Cooperative Mobile Multi-Robot Systems (CMMRS) are supposed to enable more flexible handling systems but face challenges in scalability due to kinematic overdetermination. This paper presents a scalable control architecture using admittance control to mitigate said overdetermination. A Temporal Convolutional Network (TCN) for real-time force estimation serves to mitigate instabilities in the admittance controller that occur in rigid surface contact. Experimental validation with up to eight industrial robots demonstrates high tracking accuracy, with position errors below 2 mm and orientation errors around 10 mrad.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
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2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025. IEEE Computer Society, 2025. p. 2562-2567 (IEEE International Conference on Automation Science and Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Scaling Cooperative Mobile Multi-Robot Systems for Object Handling
AU - Recker, Tobias
AU - Lachmayer, Lukas
AU - Raatz, Annika
N1 - Publisher Copyright: © 2025 IEEE.
PY - 2025/8/17
Y1 - 2025/8/17
N2 - Cooperative Mobile Multi-Robot Systems (CMMRS) are supposed to enable more flexible handling systems but face challenges in scalability due to kinematic overdetermination. This paper presents a scalable control architecture using admittance control to mitigate said overdetermination. A Temporal Convolutional Network (TCN) for real-time force estimation serves to mitigate instabilities in the admittance controller that occur in rigid surface contact. Experimental validation with up to eight industrial robots demonstrates high tracking accuracy, with position errors below 2 mm and orientation errors around 10 mrad.
AB - Cooperative Mobile Multi-Robot Systems (CMMRS) are supposed to enable more flexible handling systems but face challenges in scalability due to kinematic overdetermination. This paper presents a scalable control architecture using admittance control to mitigate said overdetermination. A Temporal Convolutional Network (TCN) for real-time force estimation serves to mitigate instabilities in the admittance controller that occur in rigid surface contact. Experimental validation with up to eight industrial robots demonstrates high tracking accuracy, with position errors below 2 mm and orientation errors around 10 mrad.
UR - http://www.scopus.com/inward/record.url?scp=105018301887&partnerID=8YFLogxK
U2 - 10.1109/CASE58245.2025.11163753
DO - 10.1109/CASE58245.2025.11163753
M3 - Conference contribution
AN - SCOPUS:105018301887
SN - 979-8-3315-2247-6
T3 - IEEE International Conference on Automation Science and Engineering
SP - 2562
EP - 2567
BT - 2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025
PB - IEEE Computer Society
T2 - 21st IEEE International Conference on Automation Science and Engineering, CASE 2025
Y2 - 17 August 2025 through 21 August 2025
ER -