A Salvage Target Tracking Algorithm for Unmanned Surface Vehicles Combining Improved Line-of-Sight and Key Point Guidance
Surface target salvage is a crucial component of marine emergencies. Although unmanned surface vehicles (USVs) have emerged as effective alternative platforms to traditional artificial salvage, salvage target tracking remains a challenging issue. Therefore, this paper proposes a salvage target track...
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| Vydáno v: | Journal of marine science and engineering Ročník 13; číslo 6; s. 1158 |
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01.06.2025
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| ISSN: | 2077-1312, 2077-1312 |
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| Abstract | Surface target salvage is a crucial component of marine emergencies. Although unmanned surface vehicles (USVs) have emerged as effective alternative platforms to traditional artificial salvage, salvage target tracking remains a challenging issue. Therefore, this paper proposes a salvage target tracking (STT) algorithm which enables rapid approach (RA) to the salvage target, while maintaining an appropriate salvage distance that keeps the surface target within the operational range in the terminal tracking (TT) phase. In the RA phase, the model predictive line-of-sight (PLOS) guidance algorithm is proposed to estimate and compensate for the drift angle encountered when following a curved path. In the TT phase, a guidance algorithm based on the key point is proposed to track the salvage target. To achieve the goals in the RA phase and the TT phase, a heading and speed controller based on proportional–integral–derivative control is proposed to track the desired signals computed by the PLOS and key point guidance algorithms. To verify the effectiveness of the STT algorithm, simulation analysis is conducted for the PLOS guidance algorithm and key point guidance algorithm. The simulation results show that the proposed PLOS guidance algorithm has the lowest cross-tracking error compared with the traditional LOS, integral LOS, and adaptive error constraint LOS. Moreover, the distance between the USV and the salvage target is less than the operating radius of the salvage operation. The results demonstrate that the proposed STT algorithm is capable of maintaining the appropriate salvage distance while tracking the salvage target. |
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| AbstractList | Surface target salvage is a crucial component of marine emergencies. Although unmanned surface vehicles (USVs) have emerged as effective alternative platforms to traditional artificial salvage, salvage target tracking remains a challenging issue. Therefore, this paper proposes a salvage target tracking (STT) algorithm which enables rapid approach (RA) to the salvage target, while maintaining an appropriate salvage distance that keeps the surface target within the operational range in the terminal tracking (TT) phase. In the RA phase, the model predictive line-of-sight (PLOS) guidance algorithm is proposed to estimate and compensate for the drift angle encountered when following a curved path. In the TT phase, a guidance algorithm based on the key point is proposed to track the salvage target. To achieve the goals in the RA phase and the TT phase, a heading and speed controller based on proportional–integral–derivative control is proposed to track the desired signals computed by the PLOS and key point guidance algorithms. To verify the effectiveness of the STT algorithm, simulation analysis is conducted for the PLOS guidance algorithm and key point guidance algorithm. The simulation results show that the proposed PLOS guidance algorithm has the lowest cross-tracking error compared with the traditional LOS, integral LOS, and adaptive error constraint LOS. Moreover, the distance between the USV and the salvage target is less than the operating radius of the salvage operation. The results demonstrate that the proposed STT algorithm is capable of maintaining the appropriate salvage distance while tracking the salvage target. |
| Audience | Academic |
| Author | Wang, Yang Zheng, Rencheng Liu, Chao Liu, Jiahe Wen, Mingmei Wang, Jinzhe |
| Author_xml | – sequence: 1 givenname: Jiahe surname: Liu fullname: Liu, Jiahe – sequence: 2 givenname: Chao orcidid: 0000-0003-4729-589X surname: Liu fullname: Liu, Chao – sequence: 3 givenname: Mingmei surname: Wen fullname: Wen, Mingmei – sequence: 4 givenname: Yang surname: Wang fullname: Wang, Yang – sequence: 5 givenname: Jinzhe surname: Wang fullname: Wang, Jinzhe – sequence: 6 givenname: Rencheng surname: Zheng fullname: Zheng, Rencheng |
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| SubjectTerms | Algorithms Analysis Control algorithms Coordinate transformations Design Distance Effectiveness key point guidance algorithm Line of sight line-of-sight guidance algorithm Methods model prediction Neural networks path following Proportional integral derivative Salvage Salvage operations salvage target tracking Salvaging Simulation analysis Speed control Surface vehicles Tracking Tracking errors Traffic accidents & safety Unmanned vehicles Vehicles |
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