Working under the Shadow of Drones: Investigating Occupational Safety Hazards among Commercial Drone Pilots

OCCUPATIONAL APPLICATIONS

A long-standing debate has surrounded the factors that lead to drone mishaps. The results of our study indicate that, from the perspective of drone pilots, situational awareness, decision-based, and skill-based errors are the primary human-factors relevant causes of drone mishaps. Additionally, deficiencies in drone interfaces should be addressed comprehensively to ensure humans can more precisely control drones. Our findings suggest that following safety procedures, receiving technical training, and flying outdoors were associated with a reduced risk of drone-related mishaps at work.

TECHNICAL ABSTRACT

Background: Commercial drones are rapidly transforming business operations, however there is a paucity of research evaluating occupational hazards and risks associated with drone deployment in the workplace.Purpose: We aimed to identify challenges of human-drone collaborations and assess drone pilot perceptions of workplace safety.Methods: An online questionnaire was generated and sent to 308 drone pilots working in different industries. A total of 75 of responses were included for data analysis. Descriptive statistics, principal component analysis, and association rule mining were employed to extract knowledge from the obtained data.Results: Our results indicate that human factors are the main contributors to workplace drone mishaps. Poor communication, information display, and control modes were found to be chief obstacles to effective human-drone collaboration. Drone pilots indicated a propensity for complying with and participating in safety practices. Following safety procedures, receiving technical training, and flying outdoors may all be associated with a lower risk of drone mishaps.Conclusions: Offering professional training to pilots and following safety procedures could decrease the risks associated with occupational drones.

Keywords:

• Drones
• occupational safety and health
• unmanned aerial vehicles (UAVs)
• machine learning
• ergonomics

Acknowledgments

The authors thank Darrell Binnion, the collaborator of this study, for his scholarly contributions. Additionally, the authors recognize Dr. Tao Yuan from the Ohio University Department of Industrial and Systems Engineering for his kind assistance in IRB preparation and approval. Moreover, the authors thank Brad Weckman for his insightful comments. Finally, the authors thank Dr. Dale Masel from the Ohio State University Department of Engineering Education for his help with funding.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the National Institute for Occupational Safety and Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.