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

arXiv: https://arxiv.org/abs/2302.10393 · HTML: https://mlsanity.com/paper/2302.10393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation](https://mlsanity.com/paper/2101.01831/) (2021)
- [Connectivity Maintenance: Global and Optimized approach through Control Barrier Functions](https://mlsanity.com/paper/2003.10178/) (2020)
- [Adaptation to Team Composition Changes for Heterogeneous Multi-Robot Sensor Coverage](https://mlsanity.com/paper/2012.09334/) (2020)
- [Distributed State Estimation Using Intermittently Connected Robot Networks](https://mlsanity.com/paper/1805.01574/) (2018)

## Built on by (most cited first, 1 of 1)

- [Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave](https://mlsanity.com/paper/2409.13533/) (2024)

## Abstract (arXiv)

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.