In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.

翻译：在搜救或灾害救援等应用中，异构多机器人系统（MRS）能够为需要多种能力的复杂目标提供显著优势。然而，在这些应用场景中，通信往往不可靠，导致大多数多机器人系统算法出现效率低下甚至完全失效的问题。许多研究者通过要求所有机器人保持通信（利用邻近约束）或假设所有机器人在长时间断连期间执行预设计划来解决这一问题。后一种方法虽然能够提升MRS的效率，但故障和环境不确定性可能在整个系统中产生级联效应，尤其是在任务目标复杂或具有时间敏感性的情况下。为解决此问题，我们提出了一种认知规划框架，使机器人能够推理系统状态、利用异构系统构成并优化向断连邻居的信息传播。动态认知逻辑形式化了信念状态的传播过程，而认知任务分配与信息博弈则通过混合整数规划实现，该规划利用信念状态进行效用预测与规划。所提框架通过异构车辆的仿真与实验进行了验证。