Beyond boundaries: Unveiling human-drone proxemic dynamics using Virtual Reality

Bretin, Robin (2025) Beyond boundaries: Unveiling human-drone proxemic dynamics using Virtual Reality. PhD thesis, University of Glasgow.

Abstract

Social drones—autonomous unmanned aerial systems operating in inhabited environments—are a rapidly developing transformative technology. While they promise significant benefits, their successful integration into everyday life depends not only on technological advancements but also on their harmonious incorporation into people’s environments. Individuals intuitively navigate their surroundings, adjusting their distances from both people and non-living entities. This raises a critical question: How will the integration of social drones affect these subtle spatial dynamics? Inappropriate spacing can lead to discomfort, stress, and even defensive reactions, such as evasive maneuvers or attacks. Therefore, before drones are deployed in inhabited spaces, it is essential to understand their spatial relationships with people—an area of study known as human–drone proxemics (HDP). Despite the importance of this issue, significant gaps remain. First, there is a lack of theoretical frameworks for interpreting human–drone proxemics. Second, there is an absence of valid methodological approaches, largely due to constraints in real-world studies. To address these gaps, this research aims to: 1) provide researchers with essential interpretive tools by establishing a solid theoretical foundation for human–drone proxemics, and 2) develop an effective approach for studying these behaviors by investigating Virtual Reality (VR) as a promising alternative to real-world studies. Following an extensive literature review on proxemics, Human–Drone Interaction (HDI), and VR, we devised a course of action. Through five user studies conducted in virtual environments specifically designed for studying proxemics, we evaluated the applicability of four frameworks that explain people’s spatial relationships with drones. Each study included theoretical grounding, empirical assessments, drone design considerations, and concrete guidelines for adopting these frameworks. Our findings reveal that distancing behaviors with external entities are shaped by various motivations—including goal oriented actions, protective instincts, social appropriateness, and arousal regulation. These motivations, influenced by how individuals perceive sensory information, often conflict, prompting physical, environmental, or cognitive adjustments to reconcile competing desires, such as the urge to approach the drone while maintaining distance. These insights culminated in a model of human proxemics with external entities, grounded in human–drone proxemics but extendable to other entities, integrating diverse motivations, highlighting their interactions, and offering new insights into the sensory processes underlying these behaviors. It equips researchers with a framework to better motivate, predict, and interpret findings in HDP studies. Additionally, our research developed a practical understanding of VR as a methodological tool for exploring HDP. VR proved to be a powerful approach, offering significant advantages over constrained real world studies. We formulated a methodological protocol and practical guidelines to equip researchers with the tools they were lacking, enabling more effective exploration of human–entity proxemics. These contributions not only fill critical gaps in the field but also provide a robust foundation for future research, fostering cumulative knowledge development in HDI.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Funder's Name: Engineering and Physical Sciences Research Council (EPSRC)
• Supervisor's Name: Khamis, Dr. Mohamed and Cross, Professor Emily
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85030
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 10 Apr 2025 07:52
• Last Modified: 10 Apr 2025 07:55
• Thesis DOI: 10.5525/gla.thesis.85030
• URI: https://theses.gla.ac.uk/id/eprint/85030

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