DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woo-Cheol | ko |
dc.contributor.author | Lim, Ming Chong | ko |
dc.contributor.author | Choi, Han-Lim | ko |
dc.date.accessioned | 2023-09-07T02:02:43Z | - |
dc.date.available | 2023-09-07T02:02:43Z | - |
dc.date.created | 2023-09-07 | - |
dc.date.issued | 2021-05-30 | - |
dc.identifier.citation | 2021 IEEE International Conference on Robotics and Automation (ICRA), pp.11508 - 11514 | - |
dc.identifier.issn | 1050-4729 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312299 | - |
dc.description.abstract | This paper presents a navigation network based deep reinforcement learning framework for autonomous indoor robot exploration. The presented method features a pattern cognitive non-myopic exploration strategy that can better reflect universal preferences for structure. We propose the Extendable Navigation Network (ENN) to encode the partially observed high-dimensional indoor Euclidean space to a sparse graph representation. The robot's motion is generated by a learned Q-network whose input is the ENN. The proposed framework is applied to a robot equipped with a 2D LIDAR sensor in the GAZEBO simulation where floor plans of real buildings are implemented. The experiments demonstrate the efficiency of the framework in terms of exploration time. | - |
dc.language | English | - |
dc.publisher | IEEE | - |
dc.title | Extendable Navigation Network based Reinforcement Learning for Indoor Robot Exploration | - |
dc.type | Conference | - |
dc.identifier.wosid | 000771405403130 | - |
dc.identifier.scopusid | 2-s2.0-85125439634 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 11508 | - |
dc.citation.endingpage | 11514 | - |
dc.citation.publicationname | 2021 IEEE International Conference on Robotics and Automation (ICRA) | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | Xi'an | - |
dc.identifier.doi | 10.1109/icra48506.2021.9561040 | - |
dc.contributor.localauthor | Choi, Han-Lim | - |
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