多机器人任务的鲁棒在线认知重规划
Robust Online Epistemic Replanning of Multi-Robot Missions
AI总结：
针对通信受限下多机器人任务易受故障与不确定性影响的问题，提出集中式间歇会合任务分配与基于认知规划和MCTS的去中心化重规划框架，并通过仿真和飞行实验验证其优于基线。
摘要：
随着多机器人系统（MRS）变得更加经济实惠且计算能力不断增强，它们为环境监测、水下检查或太空探索等复杂应用提供了显著优势。然而，在这些应用领域中考虑潜在的通信丢失或通信基础设施不可用，仍然是一个悬而未决的问题。许多适用的MRS研究假设系统能够通过邻近规则和编队控制，或通过设计一种在长时间断连期间分离并遵守预定计划的框架来维持通信。后一种技术使MRS更高效，但故障和环境不确定性可能在整个系统中产生多米诺效应，尤其是在任务目标复杂或具有时间敏感性时。为解决这一问题，我们提出的框架包含两个主要阶段：i）集中式规划器，通过奖励机器人之间的间歇会合并联来分配任务，以减轻任务执行期间意外事件的影响；ii）去中心化重规划方案，利用认知规划形式化信念传播，并在给定分布式理性信念更新的情况下，通过蒙特卡洛树搜索进行策略优化。所提框架优于基线启发式方法，并通过仿真和飞行器实验得到验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
