Low Frequency Multi-Robot Networking

doi:10.1109/ACCESS.2024.3358280

2024-01-01

DOI：10.1109/ACCESS.2024.3358280

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摘要

En 中文

Autonomous teams of unmanned ground and air vehicles rely on networking and distributed processing to collaborate as they jointly localize, explore, map, and learn in sometimes difficult and adverse conditions. Co-designed intelligent wireless networks are needed for these autonomous mobile agents for applications including disaster response, logistics and transportation, supplementing cellular networks, and agricultural and environmental monitoring. In this paper we describe recent progress on wireless networking and distributed processing for autonomous systems using a low frequency portion of the electromagnetic spectrum, here defined as roughly 25 to 100 MHz with corresponding wavelengths of 3 to 12 meters. This research is motivated by the desire to support autonomous systems operating in dense and cluttered environments by harnessing low frequency propagation, where meters long wavelengths yield significantly reduced scattering and enhanced penetration of obstacles and structures. This differs considerably from higher frequency propagation, requiring different low frequency propagation models than those widely employed for other bands. Progress in use of low frequency for autonomous systems has resulted from combined advances in low frequency propagation modeling, networking, antennas and electromagnetics, geolocation, multi-antenna array distributed beamforming, and mobile collaborative processing. This article describes the breadth and the depth of interaction between areas, leading to new tools and methods, especially in physically complex indoor/outdoor, dense urban, and other challenging scenarios. We bring together key results, models, measurements, and experiments that describe the state of the art for new uses of low frequency spectrum for multi-agent autonomy.

Keyword:

low frequency propagation

autonomy

multi-robot networking

complex environments

geolocation

distributed beamforming

parasitic arrays

cognitive radio