Robust Online Epistemic Replanning of Multi-Robot Missions

TL;DR

This paper presents a robust online epistemic replanning framework for multi-robot missions that handles communication disruptions through centralized task allocation and decentralized belief-based replanning, validated via simulations and aerial vehicle experiments.

Contribution

It introduces a novel two-phase framework combining centralized task allocation with decentralized epistemic replanning using Monte Carlo tree search, addressing communication loss in multi-robot systems.

Findings

Outperforms baseline heuristic in simulations

Effective in mitigating communication disruptions

Validated with aerial vehicle experiments

Abstract

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.