Line-of-sight in operating a small unmanned aerial vehicle : How far can a quadcopter fly in line-of-sight?

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Author(s)

  • Kai Way Li
  • Huiqiao Jia
  • Lu Peng
  • Lang Gan

Detail(s)

Original languageEnglish
Article number102898
Journal / PublicationApplied Ergonomics
Volume81
Online published12 Jul 2019
Publication statusPublished - Nov 2019

Abstract

A field study was conducted to investigate the probabilities of human participants to detect a small unmanned aerial vehicle (UAV) at a certain distance. A Phantom 4 quadcopter was remotely controlled to hover at one of the 32 pre-determined locations in the air. Thirty-two participants on the ground were requested to judge if they could see the quadcopter on a four-point scale: 1. definitely yes, 2. probably yes, 3. probably no, and 4. definitely no. The participants also responded whether they could hear the quadcopter on the same four-point scale. Logistic regression models were established to estimate the probability of detecting the quadcopter in the air, both visually and auditory. When navigating a quadcopter flying away from the operator, the sound stimulus diminished and then disappeared earlier than that of the sight of the quadcopter. The results of the study indicated that the probability of visual detection of the quadcopter at a distance of 300 m was approximately 0.3. When adopting a 50% probability of visual detection and the “definitely or probably yes” criterion, the estimated distance of line-of-sight was 245 m. The corresponding visual angle was 0.065°. The information in this study is valuable for drone operators, operator training institutes, and drone designers. The aviation authorities may also consider revising the codes or regulations for small UAV operation based on our findings.

Research Area(s)

  • Drone ergonomics, Line-of-sight, Logistic regression modeling, Unmanned aerial vehicle, Visual detection