IEEE CASE 2025

Title: Scaling Cooperative Mobile Multi-Robot Systems for Object Handling
Authors: Tobias Recker, Annika Raatz

Abstract: The demand for flexible and scalable robotic solutions in industrial settings has led to increased interest in Cooperative Mobile Multi-Robot Systems (CMMRS) for object handling. A key challenge in these systems is managing kinematic overdetermination, which can lead to excessive internal forces and reduced system stability. This paper presents a novel scalable control architecture that addresses these challenges by leveraging admittance control to reduce overdetermination while ensuring precise object manipulation. A Temporal Convolutional Network (TCN) is integrated for real-time force estimation, mitigating sensor noise without introducing phase shifts. The proposed method is validated through experiments with up to eight industrial robots transporting various rigid objects along complex trajectories. Results demonstrate the system’s ability to achieve high tracking accuracy, with position errors below 2 mm and orientation errors under 10 mrad for moderate-scale configurations. The study further explores the impact of system scaling on tracking performance, highlighting key trade-offs between scalability and control precision. This work provides a robust and adaptable framework for scalable CMMRS implementations in dynamic and unstructured environments.

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