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：在搜救或灾难救援等应用场景中，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彻底失败。许多研究者通过要求所有机器人利用邻近约束维持通信，或假设所有机器人在长时间断开连接期间执行预定计划来解决这一问题。后者方法能使多机器人系统达到更高效率，但失败和环境不确定性可能在整个系统中产生级联效应，尤其当任务目标复杂或对时间敏感时。为解决此问题，我们提出一种认知规划框架，使机器人能够推理系统状态、利用异构系统组成，并向断联邻居优化信息传播。动态认知逻辑形式化地描述了信念状态的传播，认知任务分配和信息传播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和规划。所提框架通过异构车辆的仿真和实验进行了验证。