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Article: Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds

TitleGetting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Authors
KeywordsNavigation
Robot kinematics
Collision avoidance
Uncertainty
Dynamics
Issue Date2019
PublisherInstitute 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?
AbstractOur 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 Identifierhttp://hdl.handle.net/10722/273455
ISSN
2019 Impact Factor: 3.608

 

DC FieldValueLanguage
dc.contributor.authorFan, T-
dc.contributor.authorCheng, X-
dc.contributor.authorPan, J-
dc.contributor.authorLong, P-
dc.contributor.authorLiu, W-
dc.contributor.authorYang, R-
dc.contributor.authorManocha, D-
dc.date.accessioned2019-08-06T09:29:18Z-
dc.date.available2019-08-06T09:29:18Z-
dc.date.issued2019-
dc.identifier.citationIEEE Robotics and Automation Letters, 2019, v. 4 n. 2, p. 1178-1185-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/273455-
dc.description.abstractOur 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.languageeng-
dc.publisherInstitute 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.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE 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.subjectNavigation-
dc.subjectRobot kinematics-
dc.subjectCollision avoidance-
dc.subjectUncertainty-
dc.subjectDynamics-
dc.titleGetting Robots Unfrozen and Unlost in Dense Pedestrian Crowds-
dc.typeArticle-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LRA.2019.2891491-
dc.identifier.scopuseid_2-s2.0-85065929948-
dc.identifier.hkuros300339-
dc.identifier.volume4-
dc.identifier.issue2-
dc.identifier.spage1178-
dc.identifier.epage1185-
dc.publisher.placeUnited States-

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