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Safety Concerns Emerging from Robots Navigating in Crowded Pedestrian Areas

Abstract:
Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with low- (< 0.15 ppsm), mid- (< 0.65 ppsm), and high- (< 1 ppsm) pedestrian densities. We present comparative results between two low-level obstacle avoidance methods and a baseline of shared control. Results show a 10% drop in relative time to goal on the highest density tests, and no other efficiency metric decrease. Moreover, autonomous navigation showed to be comparable to shared-control navigation with a lower relative jerk and significantly higher fluency in commands indicating high compatibility with the crowd. We conclude that the reactive controller fulfils a necessary task of fast and continuous adaptation to crowd navigation, and it should be coupled with high-level planners for environmental and situational awareness.Comment: \c{opyright}IEEE All rights reserved. IEEE-IROS-2022, Oct.23-27. Kyoto, Japa
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s12369-021-00796-4

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0001-9602-6085
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Role:
Author
ORCID:
0000-0002-6143-1432
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Role:
Author
ORCID:
0000-0002-7076-8010


Publisher:
Springer
Journal:
International Journal of Social Robotics More from this journal
Volume:
14
Issue:
2
Pages:
441-462
Publication date:
2021-06-21
DOI:
EISSN:
1875-4805
ISSN:
1875-4791


Language:
English
Keywords:
Pubs id:
2360885
Local pid:
pubs:2360885
Source identifiers:
W3174411578
Deposit date:
2026-01-24
ARK identifier:
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