In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.

翻译：在通信受限环境中，多机器人系统的部署面临两种选择：i) 为维持持续通信而牺牲操作效率（受邻近约束限制），或ii) 允许通信中断以提高环境覆盖效率，但需解决如何、何时、何处重新连接（会合问题）。本文针对后者展开研究，发现现有主流方法均假设机器人能够按预定计划执行任务，然而系统故障与环境条件变化可能导致机器人偏离计划，并在多机器人系统中产生级联效应。为此，本文提出一种协调式认知预测与规划框架，在不依赖通信的情况下实现探索覆盖、任务发现与完成及会合应用中的共识达成。动态认知逻辑作为核心组件，使机器人能够传递信念状态并理解其他智能体。通过基于人工物理框架的前沿方法，实现信念状态的传播与后续环境覆盖。该框架在多种杂乱环境下的无人地面车辆仿真与实验中均得到验证。