Neural Motion Planning for Autonomous Parking
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dongchan | - |
dc.contributor.author | Huh, Kunsoo | - |
dc.date.accessioned | 2023-05-03T09:40:41Z | - |
dc.date.available | 2023-05-03T09:40:41Z | - |
dc.date.created | 2023-04-06 | - |
dc.date.issued | 2023-04 | - |
dc.identifier.issn | 1598-6446 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/184856 | - |
dc.description.abstract | This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. An efficient expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of computational time and the number of node expanded related to algorithm performance. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS | - |
dc.title | Neural Motion Planning for Autonomous Parking | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Huh, Kunsoo | - |
dc.identifier.doi | 10.1007/s12555-022-0082-z | - |
dc.identifier.scopusid | 2-s2.0-85150074595 | - |
dc.identifier.wosid | 000950471400005 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.21, no.4, pp.1309 - 1318 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | - |
dc.citation.volume | 21 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1309 | - |
dc.citation.endPage | 1318 | - |
dc.type.rims | ART | - |
dc.type.docType | Article in Press | - |
dc.identifier.kciid | ART002941324 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Motion planning | - |
dc.subject.keywordPlus | A* algorithm | - |
dc.subject.keywordPlus | Auto encoders | - |
dc.subject.keywordPlus | Autonomous Parking | - |
dc.subject.keywordPlus | Conditional variational autoencoder | - |
dc.subject.keywordPlus | Efficient state expansion | - |
dc.subject.keywordPlus | Hybrid A* algorithm | - |
dc.subject.keywordPlus | Learn+ | - |
dc.subject.keywordPlus | Motion-planning | - |
dc.subject.keywordPlus | Neural motion planning | - |
dc.subject.keywordPlus | Planning strategies | - |
dc.subject.keywordAuthor | Autonomous parking | - |
dc.subject.keywordAuthor | conditional variational autoencoder | - |
dc.subject.keywordAuthor | efficient state expansion | - |
dc.subject.keywordAuthor | hybrid A* algorithm | - |
dc.subject.keywordAuthor | neural motion planning | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s12555-022-0082-z#article-info | - |
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