Journal article
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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(Preview, Version of record, pdf, 1.6MB, Terms of use)
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- Publisher copy:
- 10.1007/s12369-021-00796-4
Authors
- 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:
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1875-4805
- ISSN:
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1875-4791
- Language:
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English
- Keywords:
- Pubs id:
-
2360885
- Local pid:
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pubs:2360885
- Source identifiers:
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W3174411578
- Deposit date:
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2026-01-24
- ARK identifier:
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Terms of use
- Copyright date:
- 2021
- Licence:
- CC Attribution (CC BY)
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