Robust Online Epistemic Replanning of Multi-Robot Missions
arxiv(2024)
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要