Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments
This addresses the rendezvous problem for multi-robot teams in scenarios like exploration or disaster response where communication is limited, offering a novel approach to maintain coordination without relying on predetermined plans.
The paper tackles the problem of multi-robot coordination in communication-restricted environments, where robots must reconnect efficiently without constant communication, and proposes a framework using dynamic epistemic logic and frontier-based methods, validated through simulations and experiments with unmanned ground vehicles in cluttered environments.
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.