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This article presents an understanding of naive users’ perception of the communicative nature of unmanned aerial vehicle (UAV) motions refined through an iterative series of studies. This includes both what people believe the UAV is trying to communicate, and how they expect to respond through physical action or emotional response. Previous work in this area prioritized gestures from participants to the vehicle or augmenting the vehicle with additional communication modalities, rather than communicating without clear definitions of the states attempting to be conveyed. In an attempt to elicit more concrete states and better understand specific motion perception, this work includes multiple iterations of state creation, flight path refinement, and label assignment. The lessons learned in this work will be applicable broadly to those interested in defining flight paths, and within the human-robot interaction community as a whole, as it provides a base for those seeking to communicate using non-anthropomorphic robots. We found that the Negative Attitudes towards Robots Scale (NARS) can be an indicator of how a person is likely to react to a UAV, the emotional content they are likely to perceive from a message being conveyed, and it is an indicator for the personality characteristics they are likely to project upon the UAV. We also see that people commonly associate motions from other non-verbal communication situations onto UAVs. Flight specific recommendations are to use a dynamic retreating motion from a person to encourage following, use a perpendicular motion to their field of view for blocking, simple descending motion for landing, and to use either no motion or large altitude changes to encourage watching. Overall, this research explores the communication from the UAV to the bystander through its motion, to see how people respond physically and emotionally. 

This work has developed an iteratively refined understanding of participants’ natural perceptions and responses to unmanned aerial vehicle (UAV) flight paths, or gestures. This includes both what they believe the UAV is trying to communicate to them, in addition to how they expect to respond through physical action. Previous work in this area has focused on eliciting gestures from participants to communicate specific states, or leveraging gestures that are observed in the world rather than on understanding what the participants believe is being communicated and how they would respond. This work investigates previous gestures either created or categorized by participants to understand the perceived content of their communication or expected response, through categories created by participant free responses and confirmed through forced choice testing. The human-robot interaction community can leverage this work to better understand how people perceive UAV flight paths, inform future designs for non-anthropomorphic robot communications, and apply lessons learned to elicit informative labels from people who may or may not be operating the vehicle. We found that the Negative Attitudes towards Robots Scale (NARS) can be a good indicator of how we can expect a person to react to a robot. Recommendations are also provided to use motion approaching/retreating from a person to encourage following, perpendicular to their field of view for blocking, and to use either no motion or large altitude changes to encourage viewing. 

This paper presents a gesture set for communicating states to novice users from a small Unmanned Aerial System (sUAS) through an elicitation study comparing gestures created by participants recruited from the general public with varying levels of experience with an sUAS. Previous work in sUAS flight paths sought to communicate intent, destination, or emotion without focusing on concrete states such as Low Battery or Landing. This elicitation study uses a participatory design approach from human-computer interaction to understand how novice users would expect an sUAS to communicate states, and ultimately suggests flight paths and characteristics to indicate those states. We asked users from the general public (N=20) to create gestures for seven distinct sUAS states to provide insights for human-drone interactions and to present intuitive flight paths and characteristics with the expectation that the sUAS would have general commercial application for inexperienced users. The results indicate relatively strong agreement scores for three sUAS states: Landing (0.455), Area of Interest (0.265), and Low Battery (0.245). The other four states have lower agreement scores, however even they show some consensus for all seven states. The agreement scores and the associated gestures suggest guidance for engineers to develop a common set of flight paths and characteristics for an sUAS to communicate states to novice users. 

This project seeks to generate small Unmanned Aerial System (sUAS) flight paths that are broadly understood by the general population and can communicate states about both the sUAS and its understanding of the world. Previous work in sUAS flight paths has sought to communicate intent, destination, or emotion of the system without focusing on concrete states (e.g., low battery, landing, etc.). This work leverages biologically-based flight paths and experimental methodologies from human-human and human-humanoid robot interactions to assess the understanding of avian flight paths to communicate sUAS states to novice users. If successful, this work should inform: the human-robot interaction community about the perception of flight paths, sUAS manufacturers on how their systems could communicate with both operators and bystanders, and end users on ways to communicate with others when flying systems in public spaces. General design implications and future directions of work are suggested to build on the results here, which suggest that novice users gravitate towards labels they understand (draw attention and landing) while avoiding more technical labels (lost sensor). 

Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.