Probabilistic approach for conflict detection between two ROVs operating on trajectories at different depth levels
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, J. | - |
dc.contributor.author | Choi, J. | - |
dc.contributor.author | Choi, H.-T. | - |
dc.date.accessioned | 2023-12-22T08:30:34Z | - |
dc.date.available | 2023-12-22T08:30:34Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/8463 | - |
dc.description.abstract | This paper presents a probabilistic approach to estimate the collision risk between two ROVs operating on trajectories at different depth levels. For an analytically tractable formulation, a circular cylindrical boundary is considered as a safe separation zone around each ROV, and any violation of this safe zone is defined as a conflict. The conflict probability between two ROVs is then estimated over a prespecified time horizon, considering time-varying trajectory uncertainties. Specifically, for an efficient computation, the trajectory uncertainties in underwater environments are divided into horizontal and vertical uncertainties. In addition, the position uncertainty in the horizontal plane is defined as a multivariate Gaussian distribution and the depth uncertainty in the vertical direction is defined as a uniform distribution. Subsequently, the conflict probability is evaluated by multiplying the horizontal and vertical conflict probabilities that are evaluated from the divided uncertainties. Numerical simulations are performed to demonstrate the validity of the proposed approach. ? 2017 IEEE. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Probabilistic approach for conflict detection between two ROVs operating on trajectories at different depth levels | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/URAI.2017.7992791 | - |
dc.identifier.scopusid | 2-s2.0-85034233993 | - |
dc.identifier.bibliographicCitation | 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017, pp 671 - 674 | - |
dc.citation.title | 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 | - |
dc.citation.startPage | 671 | - |
dc.citation.endPage | 674 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Probabilistic approaches | - |
dc.subject.keywordPlus | Remotely Operated Vehicles (ROV) | - |
dc.subject.keywordPlus | Separation zone | - |
dc.subject.keywordPlus | Time-varying trajectories | - |
dc.subject.keywordPlus | Underwater environments | - |
dc.subject.keywordPlus | Ambient intelligence | - |
dc.subject.keywordPlus | Artificial intelligence | - |
dc.subject.keywordPlus | Intelligent robots | - |
dc.subject.keywordPlus | Probability | - |
dc.subject.keywordPlus | Remotely operated vehicles | - |
dc.subject.keywordPlus | Risk perception | - |
dc.subject.keywordPlus | Trajectories | - |
dc.subject.keywordPlus | Uncertainty analysis | - |
dc.subject.keywordPlus | Conflict probability | - |
dc.subject.keywordPlus | Multivariate Gaussian Distributions | - |
dc.subject.keywordPlus | Position uncertainties | - |
dc.subject.keywordAuthor | Conflict probability | - |
dc.subject.keywordAuthor | remotely operated vehicle (ROV) | - |
dc.subject.keywordAuthor | safe separation zone | - |
dc.subject.keywordAuthor | trajectory uncertainty | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(34103) 대전광역시 유성구 유성대로1312번길 32042-866-3114
COPYRIGHT 2021 BY KOREA RESEARCH INSTITUTE OF SHIPS & OCEAN ENGINEERING. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.