TY - GEN
T1 - Aerial flight paths for communication
T2 - 2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021
AU - Bevins, Alisha
AU - Duncan, Brittany A.
N1 - Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021/3/8
Y1 - 2021/3/8
N2 - 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.
AB - 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.
KW - Drones
KW - Gestures
KW - Motion
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85102759086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102759086&partnerID=8YFLogxK
U2 - 10.1145/3434073.3444645
DO - 10.1145/3434073.3444645
M3 - Conference contribution
AN - SCOPUS:85102759086
T3 - ACM/IEEE International Conference on Human-Robot Interaction
SP - 16
EP - 23
BT - HRI 2021 - Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
PB - IEEE Computer Society
Y2 - 8 March 2021 through 11 March 2021
ER -