Your way or my way: Improving human-robot co-navigation through robot intent and pedestrian prediction visualisations

Yu, X; Hoggenmueller, M; Tomitsch, M

Your way or my way: Improving human-robot co-navigation through robot intent and pedestrian prediction visualisations

Yu, X Hoggenmueller, M Tomitsch, M

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: ACM/IEEE International Conference on Human-Robot Interaction, 2023, pp. 211-221
Issue Date:: 2023-03-13

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Field	Value	Language
dc.contributor.author	Yu, X
dc.contributor.author	Hoggenmueller, M
dc.contributor.author	Tomitsch, M https://orcid.org/0000-0003-1998-2975
dc.date.accessioned	2024-06-07T04:17:35Z
dc.date.available	2024-06-07T04:17:35Z
dc.date.issued	2023-03-13
dc.identifier.citation	ACM/IEEE International Conference on Human-Robot Interaction, 2023, pp. 211-221
dc.identifier.isbn	9781450399647
dc.identifier.issn	2167-2148
dc.identifier.uri	http://hdl.handle.net/10453/179451
dc.description.abstract	As mobile robots enter shared urban spaces, operating in close proximity to people, this raises new challenges in terms of how these robots communicate with passers-by. Following an iterative process involving expert focus groups (n=8), we designed an augmented reality concept that visualises the robot's navigation intent and the pedestrian's predicted path. To understand the impact of path visualisations on trust, sense of agency, user experience, and robot understandability, we conducted a virtual reality evaluation (n=20). We compared visualising both robot intent and pedestrian path prediction against just visualising robot intent and a baseline without augmentation. The presence of path visualisations resulted in a signifcant improvement of trust. Triangulation of quantitative and qualitative results further highlights the impact of pedestrian path prediction visualisation on robot understandability as it allows for exploratory interaction.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP220102019
dc.relation.ispartof	ACM/IEEE International Conference on Human-Robot Interaction
dc.relation.ispartof	Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
dc.relation.isbasedon	10.1145/3568162.3576992
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Your way or my way: Improving human-robot co-navigation through robot intent and pedestrian prediction visualisations
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Provost
pubs.organisational-group	University of Technology Sydney/Provost/TD School
utslib.copyright.status	closed_access	*
dc.date.updated	2024-06-07T04:17:34Z
pubs.publication-status	Published

Abstract:

As mobile robots enter shared urban spaces, operating in close proximity to people, this raises new challenges in terms of how these robots communicate with passers-by. Following an iterative process involving expert focus groups (n=8), we designed an augmented reality concept that visualises the robot's navigation intent and the pedestrian's predicted path. To understand the impact of path visualisations on trust, sense of agency, user experience, and robot understandability, we conducted a virtual reality evaluation (n=20). We compared visualising both robot intent and pedestrian path prediction against just visualising robot intent and a baseline without augmentation. The presence of path visualisations resulted in a signifcant improvement of trust. Triangulation of quantitative and qualitative results further highlights the impact of pedestrian path prediction visualisation on robot understandability as it allows for exploratory interaction.

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http://hdl.handle.net/10453/179451