FPV Video Adaptation for UAV Collision Avoidance

Singh, Simran; Lee, Hee Won; Tran, Tuyen X.; Zhou, Yu; Sichitiu, Mihail L.; Guvenc, Ismail; Bhuyan, Arupjyoti

doi:10.1109/ojcoms.2021.3106274

FPV Video Adaptation for UAV Collision Avoidance

Journal Article · Tue Aug 24 00:00:00 EDT 2021 · IEEE Open Journal of the Communications Society

DOI:https://doi.org/10.1109/ojcoms.2021.3106274· OSTI ID:1885151

^[1]; Lee, Hee Won ^[2]; ^[3]; Zhou, Yu ^[3]; Sichitiu, Mihail L. ^[1]; ^[1]; ^[4]

North Carolina State Univ., Raleigh, NC (United States)
Samsung Electronics, New York, NY (United States)
AT&T Research Labs, Bedminster, NJ (United States)
Idaho National Lab. (INL), Idaho Falls, ID (United States)

First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1885151

Report Number(s):: INL/JOU-21-64670-Rev000

Journal Information:: IEEE Open Journal of the Communications Society, Journal Name: IEEE Open Journal of the Communications Society Journal Issue: 2 Vol. 2; ISSN 2644-125X

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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47 OTHER INSTRUMENTATION
Collision avoidance
First Person View (FPV)
UAV
safety
video adaptation

FPV Video Adaptation for UAV Collision Avoidance

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