Intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning
The invention discloses an intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning. The method comprises the steps that conventional tuples in the Markov decision-making process are expanded to construct a collision avoidance decision-mak...
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| creator | ZHENG KANGJIE ZHANG XINYU LIU YONGJIN CUI JINLONG JIANG LINGLING |
| description | The invention discloses an intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning. The method comprises the steps that conventional tuples in the Markov decision-making process are expanded to construct a collision avoidance decision-making model used for an intelligent ship in a navigation dense area under target guidance; and based on a post experience playback idea, establishing a target evaluation learning algorithm of the collision avoidance decision of the intelligent ship in the navigation dense area to train the collision avoidance decision model, so that the intelligent ship can perform decision output in the navigation dense area. According to the method, the decision-making process of the intelligent ship in the densely navigable area is converted into a target-oriented learning model, so that more reliable and intelligent technical support is provided for safe navigation of the ship in a complex marine environment.
本发明公开了一种基于目标评价学习的智能船 |
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本发明公开了一种基于目标评价学习的智能船</description><subject>PHYSICS</subject><subject>SIGNALLING</subject><subject>TRAFFIC CONTROL SYSTEMS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNzE0OgjAUBGA2Lox6h-cBSCS4wCUhGt24ck-e7VgayyuhhfNbfw7gapKZL7PMpotEOGcNJFLo7EDCszUcrRfSkADiEUzKJxTeJc_eahaFNKtPlff8tGKoR-y8pjsHaEoy8mgQCTO76XvowKMkus4WD3YBm1-usu3peGvOOQbfIgysIIhtcy2Kqiz3h11Vl_-YFzkjReU</recordid><startdate>20240712</startdate><enddate>20240712</enddate><creator>ZHENG KANGJIE</creator><creator>ZHANG XINYU</creator><creator>LIU YONGJIN</creator><creator>CUI JINLONG</creator><creator>JIANG LINGLING</creator><scope>EVB</scope></search><sort><creationdate>20240712</creationdate><title>Intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning</title><author>ZHENG KANGJIE ; ZHANG XINYU ; LIU YONGJIN ; CUI JINLONG ; JIANG LINGLING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118334908A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>PHYSICS</topic><topic>SIGNALLING</topic><topic>TRAFFIC CONTROL SYSTEMS</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHENG KANGJIE</creatorcontrib><creatorcontrib>ZHANG XINYU</creatorcontrib><creatorcontrib>LIU YONGJIN</creatorcontrib><creatorcontrib>CUI JINLONG</creatorcontrib><creatorcontrib>JIANG LINGLING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHENG KANGJIE</au><au>ZHANG XINYU</au><au>LIU YONGJIN</au><au>CUI JINLONG</au><au>JIANG LINGLING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning</title><date>2024-07-12</date><risdate>2024</risdate><abstract>The invention discloses an intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning. The method comprises the steps that conventional tuples in the Markov decision-making process are expanded to construct a collision avoidance decision-making model used for an intelligent ship in a navigation dense area under target guidance; and based on a post experience playback idea, establishing a target evaluation learning algorithm of the collision avoidance decision of the intelligent ship in the navigation dense area to train the collision avoidance decision model, so that the intelligent ship can perform decision output in the navigation dense area. According to the method, the decision-making process of the intelligent ship in the densely navigable area is converted into a target-oriented learning model, so that more reliable and intelligent technical support is provided for safe navigation of the ship in a complex marine environment.
本发明公开了一种基于目标评价学习的智能船</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | PHYSICS SIGNALLING TRAFFIC CONTROL SYSTEMS |
| title | Intelligent ship navigation dense area collision avoidance decision-making method based on target evaluation learning |
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