🤖 AI Summary
In high-density industrial environments, heterogeneous multi-robot systems are prone to path conflicts, increased waiting times, and congestion propagation due to communication delays and execution uncertainties. This work proposes the SCALE framework, which innovatively integrates robot motion characteristics into conflict resolution and constructs a generalized conjugate action-priority hypergraph (CAPH) to dynamically adjust robot priorities, enabling online generation of feasible paths and adaptive coordination. Leveraging a reactive architecture combined with an adaptive scheduling algorithm, the approach significantly reduces congestion propagation and waiting times in both simulations and a three-day real-world warehouse deployment, thereby enhancing coordination efficiency and execution robustness of heterogeneous robot fleets in complex operational scenarios.
📝 Abstract
With the increasing deployment of heterogeneous robot fleets in industrial environments, efficient coordination remains a critical challenge. Real-time path planning must simultaneously accommodate high robot densities and heterogeneous motion capabilities, while communication delays, execution uncertainties, and other disturbances may cause robots to deviate from the temporal assumptions underlying planned paths. Such deviations can lead to excessive waiting and congestion propagation across the fleet. This paper presents SCALE, a reactive online coordination framework that enables real-time planning while maintaining robust execution. Within this framework, we introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution. To mitigate the effects of disturbances, we further design a generalized Conjugate Action-Precedence Hypergraph (CAPH) that adaptively adjusts precedence relations among robots. Extensive validation experiments, together with a three-day deployment in a warehouse, demonstrate the