多机器人协作在不确定环境下推断任务上下文并高效规划。
Multi-Robot Coordination for Planning under Context Uncertainty
- 通过联合观测关键位置信息，动态推断隐藏的环境上下文。
- 两阶段算法实现上下文推断与带优先级的无碰撞路径规划。
- 适用于需要协同决策的复杂真实场景，如救援或仓储任务。
现实中的机器人常面临目标优先级依赖于操作上下文的情况。当上下文未知时，多个机器人需协作收集有信息量的观测以推断真实上下文，否则错误假设可能导致行为错位甚至危险。一旦确定真实上下文，机器人将根据其诱导的优先级顺序优化特定任务目标。本文将该问题形式化为多机器人上下文不确定随机最短路径（MR-CUSSP），通过在关键状态处的联合观测捕捉上下文相关信息。提出两阶段解决方案：(1) CIMOP（多目标规划协调推理）用于生成引导机器人前往信息丰富地标以高效推断上下文的策略；(2) LCBS（字典序冲突避免搜索）用于实现基于上下文诱导优先级的无碰撞多机器人路径规划。在三个模拟场景中评估算法，并在五台移动机器人组成的萨尔普（salp）场景设置中验证其实用性。
原文摘要 · Abstract (English)
Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather informative observations to infer the context, since acting based on an incorrect context can lead to misaligned and unsafe behavior. Once the underlying true context is inferred, the robots optimize their task-specific objectives in the preference order induced by the context. We formalize this problem as a Multi-Robot Context-Uncertain Stochastic Shortest Path (MR-CUSSP), which captures context-relevant information at landmark states through joint observations. Our two-stage solution approach is composed of: (1) CIMOP (Coordinated Inference for Multi-Objective Planning) to compute plans that guide robots toward informative landmarks to efficiently infer the true context, and (2) LCBS (Lexicographic Conflict-Based Search) for collision-free multi-robot path planning with lexicographic objective preferences, induced by the context. We evaluate the algorithms using three simulated domains and demonstrate its practical applicability using five mobile robots in the salp domain setup.
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