In this paper we present an experimental results-driven system design to enable more robust and self-deployed wireless communications for robotic systems autonomously operating in underground environments such as mines, caves, and tunnels. Subterranean environments pose severe challenges for wireless communications as wireless signal suffers extra power loss due to tunnel's curvatures; the existence of corners, junctions and large obstacles inside the mines; the changes in cross section of a passage and the tilt of sidewalls. This is especially the case when high-bandwidth and low-power wireless communications are considered as commonly found in autonomous robots. In response to these challenges, we present a multi-modal communication solution that a) relies on the integration of both dual 5.8GHz WiFi radios (high bandwidth channel), as well as 915MHz telemetry modules (low bandwidth channel), while at the same time b) utilizes both high-gain directional antennas outside of the underground environment and communication “breadcrumbs” within the subterranean setting. The communication breadcrumbs correspond to a highly integrated, lightweight and self-contained solution of dual radio WiFi and small patch 6dBi antennas, a 915MHz ultra low-power module with a 3dBi wire antenna, alongside battery for approximately 2 hours of operation. Finally, we present an integrated robotic solution – the “Aerial Scouts”- that are not only capable of autonomously exploring in the underground domain but also ferrying and dropping the aforementioned communication breadcrumbs on their own – thus autonomously extending their network as they go. For evaluation purposes we present experimental results from underground exploration missions where we relate the location of the robot, the self-deployed communications network and the measured received signal strength indicator (RSSI) over several points of the 3D reconstructed map of the environment.


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    Title :

    A Self-Deployed Multi-Channel Wireless Communications System for Subterranean Robots


    Contributors:


    Publication date :

    2020-03-01


    Size :

    36765506 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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