Priority-Based Distributed Coordination for Heterogeneous Multi-Robot Systems with Realistic Assumptions

Michele Cecchi1, Matteo Paiano1, Anna Mannucci2,3, Alessandro Palleschi1, Federico Pecora2,4, Lucia Pallottino1,
1Research Center "Enrico Piaggio", University of Pisa, 2Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden, 3Robert Bosch GmbH Corporate Research, 4Amazon Global Robotics
IEEE Robotics and Automation Letters 2021
Presented at the 2021 IEEE International Conference on Automation Science and Engineering (CASE)
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Abstract

A standing challenge in current intralogistics is to reliably, effectively, yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this article we explore a decentralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and report solutions to prevent or recover from deadlocks in specific situations. We validate the approach empirically in simulation with large, heterogeneous multi-robot fleets (with up to 100 robots) operating in both benchmark and realistic environments.

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BibTeX

@article{cecchi2021priority,
        title={Priority-based distributed coordination for heterogeneous multi-robot systems with realistic assumptions},
        author={Cecchi, Michele and Paiano, Matteo and Mannucci, Anna and Palleschi, Alessandro and Pecora, Federico and Pallottino, Lucia},
        journal={IEEE Robotics and Automation Letters},
        volume={6},
        number={3},
        pages={6131--6138},
        year={2021},
        publisher={IEEE}
      }

Acknowledgement

This work has received funding from the European Union's Horizon 2020 research and innovation program under agreement no.~732737 (ILIAD), by the Italian Ministry of Education, and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence), by Vinnova under project AutoHauler, and by the Semantic Robots KKS research profile.