Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments

TL;DR

This paper introduces a novel epistemic prediction and planning framework enabling multi-robot teams to coordinate and achieve consensus in communication-restricted environments without explicit communication, improving exploration and rendezvous tasks.

Contribution

It presents a new coordinated epistemic prediction and planning approach using dynamic epistemic logic for implicit communication among robots in challenging environments.

Findings

Effective in simulation and real-world experiments

Enables robots to coordinate without communication

Improves environmental coverage and rendezvous success

Abstract

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.