A navigation accuracy compensation algorithm for low-cost unmanned surface vehicles based on models and event triggers

Yan, Xin; Yang, Xiaofei; Feng, Beizhen; Liu, Wei; Ye, Hui; Zhu, Zhiyu; Shen, Hao; Xiang, Zhengrong

doi:10.1016/j.conengprac.2024.105896

Control Engineering Practice

2024

DOI: 10.1016/j.conengprac.2024.105896

|View full text |Cite

A navigation accuracy compensation algorithm for low-cost unmanned surface vehicles based on models and event triggers

Xin Yan,

Xiaofei Yang,

Beizhen Feng

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 13 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A method for detecting navigable areas in narrow rivers under complex reflection conditions

Zhang,

Hu,

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

The perception of unmanned surface vehicles is significantly influenced by the detection of navigable areas in narrow rivers. Conventional semantic segmentation networks are unable to resolve the numerous interferences on the water's surface, including highlights and inverted images. To solve this problem, a river surface image reflection removal generative adversarial network (RRGAN) is proposed to eliminate the interference of harsh water surface environment. The proposed RRGAN only uses a single generator to reduce the number of parameters. By adding AdaLIN layers in the generator to enhance the ability to generate low‐reflection images, the AdaLIN encoder (AdaLINE) is proposed to automatically generate normalized affine parameters. In addition, a cycle semantic consistency loss function with a single generator is proposed to ensure that the water region of the generated images remains unchanged. Finally, a two‐stage method for detecting navigable areas is proposed. In the first stage, the RRGAN is used to remove the interference on the water surface environment. In the second stage, the semantic segmentation network is used to segment the water body from the denoised image to determine the navigable areas on the water surface. The experimental results demonstrate that, in the complex and varied narrow river environment, the suggested RRGAN method can significantly reduce the reflection interference of the water surface and improve the accuracy of the water segmentation after the reflection is removed.

show abstract

A method for detecting navigable areas in narrow rivers under complex reflection conditions

Zhang,

Hu,

et al. 2024

Engineering Reports

View full text Add to dashboard Cite

show abstract

A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

Yang,

Lou,

et al. 2024

Expert Systems with Applications

View full text Add to dashboard Cite

A hyper-heuristic with deep Q-network for the multi-objective unmanned surface vehicles scheduling problem

Xu,

Shi,

Yin

et al. 2024

Neurocomputing

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A navigation accuracy compensation algorithm for low-cost unmanned surface vehicles based on models and event triggers

Cited by 13 publications

References 31 publications

A method for detecting navigable areas in narrow rivers under complex reflection conditions

A method for detecting navigable areas in narrow rivers under complex reflection conditions

A human-like collision avoidance method for USVs based on deep reinforcement learning and velocity obstacle

A hyper-heuristic with deep Q-network for the multi-objective unmanned surface vehicles scheduling problem

Contact Info

Product

Resources

About