As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles.

翻译：随着多机器人系统（MRS）成本降低及计算能力提升，其在环境监测、水下巡检、太空探索等复杂应用中展现出显著优势。然而，如何应对这些应用场景中潜在的通信中断或通信基础设施不可用的问题仍是一个开放挑战。现有MRS相关研究大多假设系统可通过邻近约束与编队控制维持通信，或通过设计分离策略在长时间断连状态下遵循预定计划。后者虽使MRS更具效率，但当任务目标复杂或具有时间敏感性时，系统故障与环境不确定性可能引发连锁反应。针对该问题，本文提出两阶段框架：i）集中式规划器通过奖励机器人间间歇性会合来分配任务，以减轻任务执行中意外事件的影响；ii）基于认知规划的去中心化重规划方案，利用信念传播形式化建模与蒙特卡洛树搜索实现分布式合理信念更新下的策略优化。该框架在基线启发式算法上表现出优越性，并通过仿真实验与飞行器实物实验验证了有效性。