基于改进混合运动麻雀搜索算法的水电站ROV路径规划
TJ630.34%U666; 水下遥控机器人(ROV)路径规划是水电站水下巡检作业的关键.针对电站水库下复杂环境及现有路径规划算法存在规划时间长、算法稳定性差、易陷入局部最优及生成路径不平滑等问题,提出一种基于改进混合运动麻雀搜索算法的水电站ROV路径规划方法.首先,引入佳点集改进麻雀种群初始化方法,提高了种群多样性;其次,提出混合运动策略优化麻雀群体位置更新方式,提高了算法收敛精度及稳定性;然后,结合工程实际问题,考虑水库下水流速度大、强磁场、障碍物以及投入成本等因素,建立了包含时间成本、路径威胁、水流扰动和偏置函数的多元目标函数;最后,采用三次B样条插值得到最优光滑路径.仿真结果表明,相较...
Saved in:
| Published in | 水下无人系统学报 Vol. 32; no. 2; pp. 320 - 327 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
国家能源集团江西电力有限公司,江西南昌,330000%国家能源集团江西电力有限公司万安水力发电厂,江西吉安,343800%中国电力建设集团(广东)工程监测检测技术有限公司,广东广州,510000
2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2096-3920 |
| DOI | 10.11993/j.issn.2096-3920.2023-0162 |
Cover
| Abstract | TJ630.34%U666; 水下遥控机器人(ROV)路径规划是水电站水下巡检作业的关键.针对电站水库下复杂环境及现有路径规划算法存在规划时间长、算法稳定性差、易陷入局部最优及生成路径不平滑等问题,提出一种基于改进混合运动麻雀搜索算法的水电站ROV路径规划方法.首先,引入佳点集改进麻雀种群初始化方法,提高了种群多样性;其次,提出混合运动策略优化麻雀群体位置更新方式,提高了算法收敛精度及稳定性;然后,结合工程实际问题,考虑水库下水流速度大、强磁场、障碍物以及投入成本等因素,建立了包含时间成本、路径威胁、水流扰动和偏置函数的多元目标函数;最后,采用三次B样条插值得到最优光滑路径.仿真结果表明,相较于其他路径规划算法,所提方法在计算精度、收敛速度和稳定性方面表现更好,适用于水电站水下巡检任务. |
|---|---|
| AbstractList | TJ630.34%U666; 水下遥控机器人(ROV)路径规划是水电站水下巡检作业的关键.针对电站水库下复杂环境及现有路径规划算法存在规划时间长、算法稳定性差、易陷入局部最优及生成路径不平滑等问题,提出一种基于改进混合运动麻雀搜索算法的水电站ROV路径规划方法.首先,引入佳点集改进麻雀种群初始化方法,提高了种群多样性;其次,提出混合运动策略优化麻雀群体位置更新方式,提高了算法收敛精度及稳定性;然后,结合工程实际问题,考虑水库下水流速度大、强磁场、障碍物以及投入成本等因素,建立了包含时间成本、路径威胁、水流扰动和偏置函数的多元目标函数;最后,采用三次B样条插值得到最优光滑路径.仿真结果表明,相较于其他路径规划算法,所提方法在计算精度、收敛速度和稳定性方面表现更好,适用于水电站水下巡检任务. |
| Abstract_FL | Path planning for underwater remotely operated vehicles(ROVs)is a prerequisite for underwater inspection operation of hydropower plants.The reservoir of hydropower plants has complex environments,and the existing path planning algorithms face the problems of long planning time,poor stability of algorithms,easy fall into the local optimum,and unsmooth path generation.In view of these issues,this paper put forward a ROV path planning method for hydropower plants based on the improved hybrid motion sparrow search algorithm.Firstly,the good point set was introduced to improve the sparrow population initialization method,which enhanced the population diversity;secondly,the hybrid motion strategy was proposed to optimize the sparrow population position updating method,increasing the algorithm's convergence accuracy and stability;then,the multivariate objective function,which contained time cost,path threat,current disturbance,and penalty function,was established by combining with the actual engineering problems and considering the factors of large flow velocity of reservoirs,strong magnetic field,obstacles,and cost;finally,the triple B-spline interpolation was used to obtain the optimal smooth path.The simulation results show that compared with other path planning algorithms,the proposed method performs better in terms of computational accuracy,convergence speed,and stability,and it is suitable for underwater inspection tasks of hydropower plants. |
| Author | 曾学文 黄秀华 周达 张福林 陈敏 |
| AuthorAffiliation | 国家能源集团江西电力有限公司,江西南昌,330000%国家能源集团江西电力有限公司万安水力发电厂,江西吉安,343800%中国电力建设集团(广东)工程监测检测技术有限公司,广东广州,510000 |
| AuthorAffiliation_xml | – name: 国家能源集团江西电力有限公司,江西南昌,330000%国家能源集团江西电力有限公司万安水力发电厂,江西吉安,343800%中国电力建设集团(广东)工程监测检测技术有限公司,广东广州,510000 |
| Author_FL | CHEN Min HUANG Xiuhua ZENG Xuewen ZHOU Da ZHANG Fulin |
| Author_FL_xml | – sequence: 1 fullname: ZENG Xuewen – sequence: 2 fullname: HUANG Xiuhua – sequence: 3 fullname: CHEN Min – sequence: 4 fullname: ZHOU Da – sequence: 5 fullname: ZHANG Fulin |
| Author_xml | – sequence: 1 fullname: 曾学文 – sequence: 2 fullname: 黄秀华 – sequence: 3 fullname: 陈敏 – sequence: 4 fullname: 周达 – sequence: 5 fullname: 张福林 |
| BookMark | eNo9j01Lw0AYhPdQwVr7K8Rj6u67ySbvUUr9gEJB1GvZNFlpKCm4iHjz0EPxIEUqohUELwqCClaCEeyfMR_9FwYUTzMMwwzPEimF_dAnZIXRGmOIfC2odbUOa0BRGByBFg64QZmAEin_p4ukqnXXpYAOCJtjmTSSu_g7Pk_HH_lskkZRMhrms1Fy9jiPP-eT03R0m03vs-er9O0yuxmkr9Ns_J49Xe-09vPoJfka5A-DZHixTBaU7Gm_-qcVsrfR2K1vGc3W5nZ9vWloRqkwpC2lT9GyTXQd21WehQJs1QHJ0XMV-MpG38KOCUWrI0zPtBzFHAUgXLTQ5BWy-rt7LEMlw4N20D86DIvH9kkv0AWySaFg5j-ckmiO |
| ClassificationCodes | TJ630.34%U666 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.11993/j.issn.2096-3920.2023-0162 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitle_FL | ROV Path Planning of Hydropower Plants Based on Improved Hybrid Motion Sparrow Search Algorithm |
| EndPage | 327 |
| ExternalDocumentID | yljs202402016 |
| GroupedDBID | 2B. 4A8 92I 93N ALMA_UNASSIGNED_HOLDINGS CDYEO PSX TCJ |
| ID | FETCH-LOGICAL-s1006-a7aae095749b87bfd59627fc2a39dbf2ef79e59c42e09c64d458f18f226b95943 |
| ISSN | 2096-3920 |
| IngestDate | Thu May 29 03:59:41 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 2 |
| Keywords | remotely operated vehicle 水下遥控机器人 sparrow search algorithm 麻雀搜索算法 path planning 路径规划 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1006-a7aae095749b87bfd59627fc2a39dbf2ef79e59c42e09c64d458f18f226b95943 |
| PageCount | 8 |
| ParticipantIDs | wanfang_journals_yljs202402016 |
| PublicationCentury | 2000 |
| PublicationDate | 2024 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – year: 2024 text: 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | 水下无人系统学报 |
| PublicationTitle_FL | Journal of Unmanned Undersea Systems |
| PublicationYear | 2024 |
| Publisher | 国家能源集团江西电力有限公司,江西南昌,330000%国家能源集团江西电力有限公司万安水力发电厂,江西吉安,343800%中国电力建设集团(广东)工程监测检测技术有限公司,广东广州,510000 |
| Publisher_xml | – name: 国家能源集团江西电力有限公司,江西南昌,330000%国家能源集团江西电力有限公司万安水力发电厂,江西吉安,343800%中国电力建设集团(广东)工程监测检测技术有限公司,广东广州,510000 |
| SSID | ssib029826739 ssib041262115 ssib030453107 ssib036436268 ssib051373269 |
| Score | 2.3696117 |
| Snippet | TJ630.34%U666; 水下遥控机器人(ROV)路径规划是水电站水下巡检作业的关键.针对电站水库下复杂环境及现有路径规划算法存在规划时间长、算法稳定性差、易陷入局部最优及生成路... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 320 |
| Title | 基于改进混合运动麻雀搜索算法的水电站ROV路径规划 |
| URI | https://d.wanfangdata.com.cn/periodical/yljs202402016 |
| Volume | 32 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals issn: 2096-3920 databaseCode: DOA dateStart: 20220101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssib030453107 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwxV3Na9RAFA-1BfEiioqfpaBz3JpMMpl5x2Q3SxFUkFZ6K8l2o4isYNuDPXnooXiQIhXRCoIXBUEFK6UV7D_jfvS_8L2X7CalW6xehCVMXn7zPmeSeW_zYVnX4nTexetQUomValQ8hcM4jl27kqZ2YkxTN5IGFfRv3vKnZrwbs2p2ZGy7dNfS0mIy2Vge-lzJv0QVaRhXekr2LyI7YIoEbGN8cYsRxu2RYiwiJaAuwkBEHm1NJCJfALZBREaEdQEhUUJNPwLbwpj-IZsoJhABUoC6hyE1sIuxmY8toCoi7OuJQFIjiARoZugKUEQBFOoxxSYYUVA6HwpCAXDn9l0Wh7t1EhdGjDci0NxQpE_2mbr-ErnEDVkZYdgElBvYuZlkryYdSGFNW2DmQU0EPoHJKNUfStw7JMllCCBKFxAgLoYNIM0y19TQoWUIAHvPJ9tNvTiCfnXYi4akhFG5kiKLGioDUZEaK4JAn3oYlyk-WUUhyQLg52DyOx5y2EL0mqLIFV7m-GUxxlAZyLXMQ4tSqtyokx9ldRgrNjMLKqAyVYS5Li0vhFT_WeE8_DqXTuDByBjwYTC6f58IBMtD7aU5AAVPtJc-h5DZyxKDWsnwg5orGio0BA37JBriAWkYBiyReaKxkoXSTMimRzayNXUh_X2Sko31wM1n4ICCovM5WeWJdMRwH9ChoLAmQLdCK_7vrVgQSBv8CuYzdnn1Uvw7MCgiZUsRN8c18z09fMEE_Da5ByxhciABW5Lus8xXSvvfSP_k4YMFmj-YaTr-MWtMUim0VM3CS7kEI31dvHCY7sdwS5UmFxMhXxaVDs-RvnSKzEQ5rsbEkooXA52OW1f7Gl8_XF9--LKVxq17pTxh-pR1Mk_wJ4LsbH3aGlm-f8aK2u92fu0876xv93Y3Oltb7bXV3u5a-9nHvZ0fextPO2tvu5vvu59fdb697L5Z6Xzd7K5_7356jWfP3taX9s-V3oeV9uqLs9ZMPZquTlXyD9hUFhyq1MY6jpuYw2oPEqOTdJ4_dZY2ZOzCfJLKZqqhqaDhSUQ1fG8er5WpY1JMiRNQ4LnnrNHWo1bzvDXRkNLxTRpjhmw8O01iSGJXpSkmeDrWbnLBGs_NnssvUAtz--J08U-AS9YJamfl5cvW6OLjpeYVTLgWk3EO7W8Ozwm1 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E6%94%B9%E8%BF%9B%E6%B7%B7%E5%90%88%E8%BF%90%E5%8A%A8%E9%BA%BB%E9%9B%80%E6%90%9C%E7%B4%A2%E7%AE%97%E6%B3%95%E7%9A%84%E6%B0%B4%E7%94%B5%E7%AB%99ROV%E8%B7%AF%E5%BE%84%E8%A7%84%E5%88%92&rft.jtitle=%E6%B0%B4%E4%B8%8B%E6%97%A0%E4%BA%BA%E7%B3%BB%E7%BB%9F%E5%AD%A6%E6%8A%A5&rft.au=%E6%9B%BE%E5%AD%A6%E6%96%87&rft.au=%E9%BB%84%E7%A7%80%E5%8D%8E&rft.au=%E9%99%88%E6%95%8F&rft.au=%E5%91%A8%E8%BE%BE&rft.date=2024&rft.pub=%E5%9B%BD%E5%AE%B6%E8%83%BD%E6%BA%90%E9%9B%86%E5%9B%A2%E6%B1%9F%E8%A5%BF%E7%94%B5%E5%8A%9B%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8%2C%E6%B1%9F%E8%A5%BF%E5%8D%97%E6%98%8C%2C330000%25%E5%9B%BD%E5%AE%B6%E8%83%BD%E6%BA%90%E9%9B%86%E5%9B%A2%E6%B1%9F%E8%A5%BF%E7%94%B5%E5%8A%9B%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8%E4%B8%87%E5%AE%89%E6%B0%B4%E5%8A%9B%E5%8F%91%E7%94%B5%E5%8E%82%2C%E6%B1%9F%E8%A5%BF%E5%90%89%E5%AE%89%2C343800%25%E4%B8%AD%E5%9B%BD%E7%94%B5%E5%8A%9B%E5%BB%BA%E8%AE%BE%E9%9B%86%E5%9B%A2%28%E5%B9%BF%E4%B8%9C%29%E5%B7%A5%E7%A8%8B%E7%9B%91%E6%B5%8B%E6%A3%80%E6%B5%8B%E6%8A%80%E6%9C%AF%E6%9C%89%E9%99%90%E5%85%AC%E5%8F%B8%2C%E5%B9%BF%E4%B8%9C%E5%B9%BF%E5%B7%9E%2C510000&rft.issn=2096-3920&rft.volume=32&rft.issue=2&rft.spage=320&rft.epage=327&rft_id=info:doi/10.11993%2Fj.issn.2096-3920.2023-0162&rft.externalDocID=yljs202402016 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fyljs%2Fyljs.jpg |