  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.
