Heading and velocity guidance based path following of autonomous surface vehicle with uncertainty attenuation and asymmetric saturated constraints

Yu, Yalei; Guo, Chen; Li, Tieshan; Shen, Haiqing

doi:10.1016/j.isatra.2023.02.005

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…On the other hand, the path following algorithm attempts to trace a predefined geometric path instead of following a time-related path (Peng et al, 2023;Murali et al, 2022). Since the path following algorithm does not require time constraints, it is widely preferred in the motion control of USVs (Yu et al, 2023;Gonzalez et al, 2022;Xia et al, 2022;Yang et al, 2022). Path following algorithm consists of three parts: navigation, guidance law and control (Keat et al, 2022;Hung et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intelligent vector-based path following guidance law for unmanned surface vehicles

ÜNAL¹,

Akkaş²,

Atali³

2023

Preprint

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This study proposes a significant improvement on the classical vector field guidance law. Classical vector field method results in imaginary number some cases. In numerical calculation, imaginary part of the number ignores to obtain well-posed solution. However, negligence of imaginary part negatively affects the optimization workflow and it leads to larger cross tracking errors. In this study, classical vector field method was modified to obtain real numbers in all cases. Owing to the modified new method, cross tracking errors were diminished by 10% compared to the traditional vector field method. Second contribution of this study is to integrate the surrogate optimization technique to the developed robust vector field method in order to tune parameters of the guidance law. To the best of our knowledge, we are the first to combine surrogate optimization method with vector field path following algorithm. Vector field path following algorithm requires optimizing four important key parameters. While the unmanned surface vehicle performs the multi-command tasks, determining the optimum values of these four key parameters for each mission increases the computational costs significantly. Since the surrogate optimization method is more suitable for the time-consuming objective functions, it is preferred instead of the most popular optimization technique, genetic algorithm. Surrogate optimization method integrated robust vector field path following algorithm determines optimum parameters approximately 10 times faster than the genetic algorithm integrated vector field method. In addition to the time advantage of the developed model, the proposed method provides a navigation that causes less cross tracking errors compared to classical technique.

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Section: Introductionmentioning

confidence: 99%

Intelligent vector-based path following guidance law for unmanned surface vehicles

ÜNAL¹,

Akkaş²,

Atali³

2023

Preprint

View full text Add to dashboard Cite

show abstract

Intelligent vector-based path following guidance law for unmanned surface vehicles

Ünal,

Akkaş,

Atalı

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study proposes a significant improvement on the classical vector field guidance law. The classical vector field method results in an imaginary number in some cases. In the numerical calculation, the imaginary part of the number is ignored in order to obtain a solution. Neglecting the imaginary part adversely impacts the optimization process, resulting in increased cross tracking errors. The first objective of this study is to modify the classical vector field method in order to obtain real numbers in all scenarios. Owing to this modification, cross tracking errors were reduced by 10% compared to the traditional vector field method. The second goal of this study is to incorporate the surrogate optimization technique into the guidance laws developed in this study (the modified vector field and the robust vector field) to quickly tune the parameters of these guidance laws. The classical vector field and other algorithm improved in this study require optimizing four key parameters. While the unmanned surface vehicle performs the multi-command tasks, determining the optimum values of these four key parameters for each mission increases the computational costs significantly. The proposed intelligent vector-based path following guidance law (which integrates the surrogate optimization technique with the robust vector field) determines optimal parameters approximately 10 times faster than the traditional method (which integrates the genetic algorithm with the classical vector field guidance law).

show abstract

Heading and velocity guidance based path following of autonomous surface vehicle with uncertainty attenuation and asymmetric saturated constraints

Cited by 2 publications

References 55 publications

Intelligent vector-based path following guidance law for unmanned surface vehicles

Intelligent vector-based path following guidance law for unmanned surface vehicles

Intelligent vector-based path following guidance law for unmanned surface vehicles

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