Radio Propagation Environment Modeling and Learning for Networked Multi-Robot Exploration

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 communicationrestricted 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 logdistance path loss model.

复杂未知环境下的多机器人勘探任务，可借助机器人间通信提供的协同协作能力实现性能提升。精准的无线电信号强度预测，能够支撑具备通信感知能力的勘探工作。那些忽略环境对信号传播影响，或依赖先验地图的模型，在未知且通信受限（如地下）的环境中表现欠佳。本研究提出传播环境建模与学习（Propagation Environment Modeling and Learning，PropEM-L）框架，该框架依托传感器实时生成的环境三维几何表征，提取无线电节点间的视距信息以及会造成信号衰减的墙体/障碍物相关数据，从而精准预测接收信号强度（Received Signal Strength，RSS）。本数据驱动方法融合了经典信号传播现象模型（如阴影效应、反射、衍射）与机器学习的优势，且可在线适配全新环境。我们在面向2021年DARPA地下挑战赛搭建的、兼具地铁、矿井与洞穴特征的通信受限环境中，依托六机器人团队验证了PropEM-L的性能。研究结果表明，相较于对数距离路径损耗模型，PropEM-L可将信号强度预测精度提升最高达44%。

搜集汇总

背景与挑战

背景概述

该数据集专注于无线电传播环境建模与学习，用于多机器人探索系统。它利用实时传感器数据构建3D环境几何表示，结合信号传播模型和机器学习，以在线适应新环境并提高接收信号强度预测的准确性，特别针对通信受限的地下环境，实验表明其预测精度比传统模型提升高达44%。