Study on path planning of unmanned surface vessel based on data-driven genetic algorithm
The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algori...
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2019-06-01
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| Series: | 智能科学与技术学报 |
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| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926 |
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| _version_ | 1846171169722466304 |
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| author | Junfeng XIN Yongbo ZHANG Jiageng BO Bowen ZHAO Shiyuan FAN |
| author_facet | Junfeng XIN Yongbo ZHANG Jiageng BO Bowen ZHAO Shiyuan FAN |
| author_sort | Junfeng XIN |
| collection | DOAJ |
| description | The genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed,which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm,the LCPGA increases the diversity of the population,avoids falling into local optimum more effectively,and improves the accuracy,robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel. |
| format | Article |
| id | doaj-art-1e30d69a935e4662a8910522aa116147 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2019-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-1e30d69a935e4662a8910522aa1161472024-11-11T06:51:10ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522019-06-01117118059635158Study on path planning of unmanned surface vessel based on data-driven genetic algorithmJunfeng XINYongbo ZHANGJiageng BOBowen ZHAOShiyuan FANThe genetic algorithm (GA) is an effective method for the path planning system of unmanned surface vessel (USV),but it is easy to fall into local optimal precocity and converges slowly.For this,without increasing the complexity of the algorithm,a data-driven linear changing parameters genetic algorithm (LCPGA) was proposed,which can adjust adaptively control parameters in the shortest time.Compared with the traditional genetic algorithm,the LCPGA increases the diversity of the population,avoids falling into local optimum more effectively,and improves the accuracy,robustness and convergence speed of path planning.Then simulation experiments and field tests verify the more excellent performance of the LCPGA.This algorithm can be helpful in path planning for unmanned surface vessel.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926path planning;improved genetic algorithm;unmanned surface vessel;self-adaption |
| spellingShingle | Junfeng XIN Yongbo ZHANG Jiageng BO Bowen ZHAO Shiyuan FAN Study on path planning of unmanned surface vessel based on data-driven genetic algorithm 智能科学与技术学报 path planning;improved genetic algorithm;unmanned surface vessel;self-adaption |
| title | Study on path planning of unmanned surface vessel based on data-driven genetic algorithm |
| title_full | Study on path planning of unmanned surface vessel based on data-driven genetic algorithm |
| title_fullStr | Study on path planning of unmanned surface vessel based on data-driven genetic algorithm |
| title_full_unstemmed | Study on path planning of unmanned surface vessel based on data-driven genetic algorithm |
| title_short | Study on path planning of unmanned surface vessel based on data-driven genetic algorithm |
| title_sort | study on path planning of unmanned surface vessel based on data driven genetic algorithm |
| topic | path planning;improved genetic algorithm;unmanned surface vessel;self-adaption |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.201926 |
| work_keys_str_mv | AT junfengxin studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm AT yongbozhang studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm AT jiagengbo studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm AT bowenzhao studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm AT shiyuanfan studyonpathplanningofunmannedsurfacevesselbasedondatadrivengeneticalgorithm |