Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm
Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/12/2320 |
| _version_ | 1797380409334431744 |
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| author | Xiuling Wang Yong Yin Qianfeng Jing |
| author_facet | Xiuling Wang Yong Yin Qianfeng Jing |
| author_sort | Xiuling Wang |
| collection | DOAJ |
| description | Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified. |
| first_indexed | 2024-03-08T20:37:46Z |
| format | Article |
| id | doaj.art-5f802e3616c043ad9b92aa3e10658fd2 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T20:37:46Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-5f802e3616c043ad9b92aa3e10658fd22023-12-22T14:18:55ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-12-011112232010.3390/jmse11122320Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug AlgorithmXiuling Wang0Yong Yin1Qianfeng Jing2Navigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaDue to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified.https://www.mdpi.com/2077-1312/11/12/2320unmanned surface vehicle (USV)maritime searchpath planningBug algorithm |
| spellingShingle | Xiuling Wang Yong Yin Qianfeng Jing Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm Journal of Marine Science and Engineering unmanned surface vehicle (USV) maritime search path planning Bug algorithm |
| title | Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm |
| title_full | Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm |
| title_fullStr | Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm |
| title_full_unstemmed | Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm |
| title_short | Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm |
| title_sort | maritime search path planning method of an unmanned surface vehicle based on an improved bug algorithm |
| topic | unmanned surface vehicle (USV) maritime search path planning Bug algorithm |
| url | https://www.mdpi.com/2077-1312/11/12/2320 |
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