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Proceedings Paper

Toward autonomous rotorcraft flight in degraded visual environments: experiments and lessons learned
Author(s): Adam Stambler; Spencer Spiker; Marcel Bergerman; Sanjiv Singh
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Paper Abstract

Unmanned cargo delivery to combat outposts will inevitably involve operations in degraded visual environments (DVE). When DVE occurs, the aircraft autonomy system needs to be able to function regardless of the obscurant level. In 2014, Near Earth Autonomy established a baseline perception system for autonomous rotorcraft operating in clear air conditions, when its m3 sensor suite and perception software enabled autonomous, no-hover landings onto unprepared sites populated with obstacles. The m3’s long-range lidar scanned the helicopter’s path and the perception software detected obstacles and found safe locations for the helicopter to land. This paper presents the results of initial tests with the Near Earth perception system in a variety of DVE conditions and analyzes them from the perspective of mission performance and risk. Tests were conducted with the m3’s lidar and a lightweight synthetic aperture radar in rain, smoke, snow, and controlled brownout experiments. These experiments showed the capability to penetrate through mild DVE but the perceptual capabilities became degraded with the densest brownouts. The results highlight the need for not only improved ability to see through DVE, but also for improved algorithms to monitor and report DVE conditions.

Paper Details

Date Published: 13 May 2016
PDF: 12 pages
Proc. SPIE 9839, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2016, 983904 (13 May 2016); doi: 10.1117/12.2223474
Show Author Affiliations
Adam Stambler, Near Earth Autonomy, Inc. (United States)
Spencer Spiker, Near Earth Autonomy, Inc. (United States)
Marcel Bergerman, Near Earth Autonomy, Inc. (United States)
Sanjiv Singh, Near Earth Autonomy, Inc. (United States)


Published in SPIE Proceedings Vol. 9839:
Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2016
Jack Sanders-Reed; Jarvis J. Arthur, Editor(s)

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