@inproceedings{8f2910ca79f3499aac102e25e99a47ea,
title = "Scaling Cooperative Mobile Multi-Robot Systems for Object Handling",
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.",
author = "Tobias Recker and Lukas Lachmayer and Annika Raatz",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 21st IEEE International Conference on Automation Science and Engineering, CASE 2025, CASE 2025 ; Conference date: 17-08-2025 Through 21-08-2025",
year = "2025",
month = aug,
day = "17",
doi = "10.1109/CASE58245.2025.11163753",
language = "English",
isbn = "979-8-3315-2247-6",
series = "IEEE International Conference on Automation Science and Engineering",
publisher = "IEEE Computer Society",
pages = "2562--2567",
booktitle = "2025 IEEE 21st International Conference on Automation Science and Engineering, CASE 2025",
address = "United States",
}