The design space of social drones, where autonomous flyers operate in close proximity to human users or bystanders, is distinct from use cases involving a remote human operator and/or an uninhabited environment and warrants fore-grounding human-centered design concerns. Using this setup, we aim to advance our understanding of the psychology and ergonomics of interacting with autonomous social drones through experiments, and extract design implications. We present this apparatus and its rationale in this paper. For empirical design research using embodied, co-located drones, we have been developing an experimental setup that enables high precision drone control, as well as rich multimodal data collection and analysis, in an integrated fashion. Currently, in lieu of empirical studies with autonomous embodied agents, approaches such as Wizard of Oz methods, questionnaires, videos, and/or makeshift mechanisms are often employed to investigate interactions with social drones. The design of social drones, in terms of both aesthetics and behavior, can involve particular human factors that require further study. Social drones, which we define as autonomous drones that operate in close proximity to human users or bystanders, are distinct from these. The materials and results of our analysis are also published in an open online repository that intends to serve as a living hub for a community of researchers and designers working with social drones.Ĭurrent use cases for drones often involve a remote human operator and/or an environment which is inaccessible to humans. These results have thepotential to inform and expedite future research and practice,by supporting readers in defining and situating their futurecontributions. Our analysis identifiedthree high-level themes that sketch out knowledge clustersin the literature, and twelve design concerns which unpackhow various dimensions of drone aesthetics and behaviorrelate to pertinent human responses.

Recently, re-search on social drones has followed a trend of rapid growth.This paper consolidates the current state of the art in human-centered design knowledge about social drones through areview of relevant studies, scaffolded by a descriptive frame-work of design knowledge creation.

The design space of social drones, where autonomous flyersoperate in close proximity to human users or bystanders,is distinct from use cases involving a remote human opera-tor and/or an uninhabited environment and warrants fore-grounding human-centered design concerns. Finally, the presented results show the need to consider uncertainty (e.g., traction loss) in future interface design. The main results of this comparison showed that: (1) AAI led to a significant reduction in workload and a significant increase in usability and robot safety (2) the use of the PI caused a significant increase in path length, indicating that operators overshoot their goals more often with this approach (3) PI and AAI had lower reported effort and (4) AAI is more flexible and effortless than PI and CI. The user study compared AAI and PI to a Control Interface (CI), which did not display any augmented elements. This paper proposes three augmented teleoperation interfaces that allow the user to operate a robot subject to 3 seconds of latency: (1) Avatar-Aided Interface (AAI), a semi-autonomous approach based on a virtual element (2) Predictive Interface (PI), an approach with direct control and predictive elements and (3) Hybrid Interface (HI), where the operator can easily switch between PI and AAI.We conducted a systematic within-subject experiment to evaluate the proposed interfaces in a realistic virtual environment with frequent traction losses. Thus, novel operational strategies are necessary to cope with these effects. Still, even small amounts of latency can significantly affect real-time operations, often leading to compromised robot safety, goal overshoot, and high levels of human mental fatigue and cognitive workload. Adding human cognitive skills to planetary exploration through remote teleoperation can lead to more effective and valuable scientific data acquisition.

Taskboard architecture material used