Abstract
Safety is crucial for autonomous drones to operate close to humans. Besides avoiding unwanted or harmful contact, people should also perceive the drone as safe. Existing safe motion planning approaches for autonomous robots, such as drones, have primarily focused on ensuring physical safety, e.g., by imposing constraints on motion planners. However, studies indicate that ensuring physical safety does not necessarily lead to perceived safety. Prior work in Human-Drone Interaction (HDI) shows that factors such as the drone's speed and distance to the human are important for perceived safety. Building on these works, we propose a parameterized control barrier function (CBF) that constrains the drone's maximum deceleration and minimum distance to the human and update its parameters on people's ratings of perceived safety. We describe an implementation and evaluation of our approach. Results of a withinsubject user study (Ng= 15) show that we can improve perceived safety of a drone by adjusting to people individually.
Original language | English |
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Title of host publication | HRI 2023 - Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction |
Publisher | IEEE |
Pages | 446-455 |
ISBN (Electronic) | 9781450399647 |
DOIs | |
Publication status | Published - 2023 |
Externally published | Yes |
Event | 18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023 - Stockholm, Sweden Duration: 13 Mar 2023 → 16 Mar 2023 |
Conference
Conference | 18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023 |
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Country/Territory | Sweden |
City | Stockholm |
Period | 13/03/23 → 16/03/23 |
Keywords
- control barrier functions
- human-drone interaction
- motion planning
- perceived safety