PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

TL;DR

PropEM-L is a real-time, data-driven framework that models radio signal propagation in unknown environments for multi-robot exploration, significantly improving prediction accuracy over traditional models.

Contribution

It introduces a novel online learning framework that combines environmental geometry with signal propagation models for accurate RSS prediction in unknown environments.

Findings

Improves signal strength prediction accuracy by up to 44%.

Effectively adapts to new environments in real-time.

Demonstrated on a six-robot team in subterranean environments.

Abstract

Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication. Accurate radio signal strength prediction enables communication-aware exploration. Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-restricted (e.g. subterranean) environments. In this work, we present Propagation Environment Modeling and Learning (PropEM-L), a framework which leverages real-time sensor-derived 3D geometric representations of an environment to extract information about line of sight between radios and attenuating walls/obstacles in order to accurately predict received signal strength (RSS). Our data-driven approach combines the strengths of well-known models of signal propagation phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and can adapt online to new environments. We demonstrate the performance of PropEM-L on a six-robot team in a communication-restricted environment with subway-like, mine-like, and cave-like characteristics, constructed for the 2021 DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve signal strength prediction accuracy by up to 44% over a log-distance path loss model.