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Increasing perceived safety in motion planning for human-drone interaction
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-3729-157x
KTH.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-2212-4325
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-1170-7162
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Number of Authors: 52023 (English)In: HRI 2023: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, p. 446-455Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2023. p. 446-455
Keywords [en]
control barrier functions, human-drone interaction, motion planning, perceived safety
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-333381DOI: 10.1145/3568162.3576966Scopus ID: 2-s2.0-85150349732OAI: oai:DiVA.org:kth-333381DiVA, id: diva2:1785045
Conference
18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023, Stockholm, Sweden, Mar 13 2023 - Mar 16 2023
Note

Part of ISBN 9781450399647

QC 20230801

Available from: 2023-08-01 Created: 2023-08-01 Last updated: 2023-08-01Bibliographically approved

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van Waveren, SanneRudling, RasmusLeite, IolandaJensfelt, PatricPek, Christian

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