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- Publisher Website: 10.1109/LRA.2019.2891491
- Scopus: eid_2-s2.0-85065929948
- WOS: WOS:000459538100001
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Article: Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Title | Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds |
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Authors | |
Keywords | Navigation Robot kinematics Collision avoidance Uncertainty Dynamics |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE |
Citation | IEEE Robotics and Automation Letters, 2019, v. 4 n. 2, p. 1178-1185 How to Cite? |
Abstract | Our goal is to navigate a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging environments, existing approaches suffer from two common problems: the robot may get frozen and cannot make any progress toward its goal, or it may get lost due to severe occlusions inside a crowd. Here, we propose a navigation framework that handles the robot freezing and the navigation lost problems simultaneously. First, we enhance the robot's mobility and unfreeze the robot in the crowd using a reinforcement learning-based local navigation policy developed in our previous work which naturally takes into account the coordination between robots and humans. Second, the robot takes advantage of its excellent local mobility to recover from its localization failure. In particular, it dynamically chooses to approach a set of recovery positions with rich features. To the best of our knowledge, our method is the first approach that simultaneously solves the freezing problem and the navigation lost problem in dense crowds. We evaluate our method in both simulated and real-world environments and demonstrate that it outperforms the state-of-the-art approaches. Videos are available at https://sites.google.com/view/rlslam. |
Persistent Identifier | http://hdl.handle.net/10722/273455 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.119 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fan, T | - |
dc.contributor.author | Cheng, X | - |
dc.contributor.author | Pan, J | - |
dc.contributor.author | Long, P | - |
dc.contributor.author | Liu, W | - |
dc.contributor.author | Yang, R | - |
dc.contributor.author | Manocha, D | - |
dc.date.accessioned | 2019-08-06T09:29:18Z | - |
dc.date.available | 2019-08-06T09:29:18Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Robotics and Automation Letters, 2019, v. 4 n. 2, p. 1178-1185 | - |
dc.identifier.issn | 2377-3766 | - |
dc.identifier.uri | http://hdl.handle.net/10722/273455 | - |
dc.description.abstract | Our goal is to navigate a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging environments, existing approaches suffer from two common problems: the robot may get frozen and cannot make any progress toward its goal, or it may get lost due to severe occlusions inside a crowd. Here, we propose a navigation framework that handles the robot freezing and the navigation lost problems simultaneously. First, we enhance the robot's mobility and unfreeze the robot in the crowd using a reinforcement learning-based local navigation policy developed in our previous work which naturally takes into account the coordination between robots and humans. Second, the robot takes advantage of its excellent local mobility to recover from its localization failure. In particular, it dynamically chooses to approach a set of recovery positions with rich features. To the best of our knowledge, our method is the first approach that simultaneously solves the freezing problem and the navigation lost problem in dense crowds. We evaluate our method in both simulated and real-world environments and demonstrate that it outperforms the state-of-the-art approaches. Videos are available at https://sites.google.com/view/rlslam. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE | - |
dc.relation.ispartof | IEEE Robotics and Automation Letters | - |
dc.rights | IEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Navigation | - |
dc.subject | Robot kinematics | - |
dc.subject | Collision avoidance | - |
dc.subject | Uncertainty | - |
dc.subject | Dynamics | - |
dc.title | Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds | - |
dc.type | Article | - |
dc.identifier.email | Pan, J: jpan@cs.hku.hk | - |
dc.identifier.authority | Pan, J=rp01984 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/LRA.2019.2891491 | - |
dc.identifier.scopus | eid_2-s2.0-85065929948 | - |
dc.identifier.hkuros | 300339 | - |
dc.identifier.volume | 4 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1178 | - |
dc.identifier.epage | 1185 | - |
dc.identifier.isi | WOS:000459538100001 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 2377-3766 | - |