Multi-Robot Strategies for Communication-Constrained Exploration and Electrostatic Anomaly Characterization

TL;DR

This paper compares multi-robot communication strategies for exploration in communication-limited environments and introduces an electrostatic anomaly prediction algorithm, demonstrating that decentralized rendezvous improves exploration and data sharing enhances anomaly prediction.

Contribution

It provides a comparative analysis of decentralized and centralized communication strategies and introduces an electrostatic anomaly prediction algorithm for multi-robot systems.

Findings

Decentralized rendezvous outperforms fixed base station in exploration speed.

Inter-robot data sharing improves electrostatic anomaly prediction.

Decentralized approach reduces data loss risk during exploration.

Abstract

Exploration of extreme or remote environments such as Mars is often recognized as an opportunity for multi-robot systems. However, this poses challenges for maintaining robust inter-robot communication without preexisting infrastructure. It may be that robots can only share information when they are physically in close proximity with each other. At the same time, atmospheric phenomena such as dust devils are poorly understood and characterization of their electrostatic properties is of scientific interest. We perform a comparative analysis of two multi-robot communication strategies: a distributed approach, with pairwise intermittent rendezvous, and a centralized, fixed base station approach. We also introduce and evaluate the effectiveness of an algorithm designed to predict the location and strength of electrostatic anomalies, assuming robot proximity. Using an agent-based simulation, we assess the performance of these strategies in a 2D grid cell representation of a Martian environment. Results indicate that a decentralized rendezvous system consistently outperforms a fixed base station system in terms of exploration speed and in reducing the risk of data loss. We also find that inter-robot data sharing improves performance when trying to predict the location and strength of an electrostatic anomaly. These findings indicate the importance of appropriate communication strategies for efficient multi-robot science missions.