  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.
