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.

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

Palleschi A.;Pallottino L.
2021

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.
Cecchi, M.; Paiano, M.; Mannucci, A.; Palleschi, A.; Pecora, F.; Pallottino, L.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/1105542
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