解决工业场景下异构机器人队列实时协调难题，提升运行可靠性。
From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments
- 基于动态冲突消减机制实现在线路径生成与冲突化解
- 通过自适应调整优先关系，降低通信延迟与执行偏差影响
- 在真实仓库环境持续运行3天，验证了大规模部署的可行性
随着异构机器人队列在工业环境中日益普及，高效协同仍是一大挑战。实时路径规划需同时应对高密度机器人和多样化的运动能力，而通信延迟、执行不确定性等扰动可能导致机器人偏离规划路径所依赖的时间假设，进而引发过度等待和拥堵传播。本文提出SCALE框架，一种支持实时规划与鲁棒执行的反应式在线协调方法。该框架引入运动诱导冲突消减机制，支持在线生成可行路径以实现冲突即时化解；并设计广义共轭动作-优先超图（CAPH），可自适应调整机器人间的优先关系以缓解扰动影响。大量验证实验及为期三天的真实仓库部署结果表明，该方法在复杂工业环境下具备良好的可扩展性与可靠性。
原文摘要 · Abstract (English)
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
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