Data-driven fuzzy logic control method for improved USV path planning

The parameters of the path planning algorithm for unmanned surface vehicles (USVs) usually rely on manual settings, which makes it difficult for the algorithm to achieve the optimal solution when considering multiple factors in path planning. This paper proposes a data-driven method to improve the U...

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Veröffentlicht in:The Journal of supercomputing Jg. 81; H. 7; S. 844
Hauptverfasser: Wang, Feng, Wang, Chenglong, Wang, Yuanhui, Chemori, Ahmed, Zhang, Xiaoyue, Zhang, Kun, Zhang, Yuxuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer Nature B.V 09.05.2025
Springer Verlag
Schlagworte:
Algorithms
Automatic
Automatic Control Engineering
Computer Science
Control algorithms
Control methods
Datasets
Engineering Sciences
Fuzzy control
Fuzzy logic
Kinematics
Neural networks
Parameters
Path planning
Robotics
Surface vehicles
Unmanned vehicles
Velocity
Fuzzy logic control
Path planning
Data-driven
Neural network
Dynamic Window Approach
Unmanned Surface Vehicles (USVs)
ISSN:1573-0484, 0920-8542, 1573-0484
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Abstract The parameters of the path planning algorithm for unmanned surface vehicles (USVs) usually rely on manual settings, which makes it difficult for the algorithm to achieve the optimal solution when considering multiple factors in path planning. This paper proposes a data-driven method to improve the USV path planning algorithm in response to the problem of unreasonable control caused by manual experience parameter settings in traditional algorithms. Firstly, a dataset is constructed by extracting corresponding parameters from traditional fuzzy logic control algorithms. Then, using two existing fuzzy controllers as samples, a fuzzy neural network is designed. Finally, using this dataset, a new fuzzy logic controller is generated through a fuzzy neural network. Compared with traditional fuzzy logic controllers, data-driven controllers exhibit a more reasonable distribution of variable parameters, thus verifying the superiority of neural network-based controllers. Numerical simulations show that the proposed method improves both the path length and the navigation time, while ensuring safety in complex environments.
AbstractList The parameters of the path planning algorithm for unmanned surface vehicles (USVs) usually rely on manual settings, which makes it difficult for the algorithm to achieve the optimal solution when considering multiple factors in path planning. This paper proposes a data-driven method to improve the USV path planning algorithm in response to the problem of unreasonable control caused by manual experience parameter settings in traditional algorithms. Firstly, a dataset is constructed by extracting corresponding parameters from traditional fuzzy logic control algorithms. Then, using two existing fuzzy controllers as samples, a fuzzy neural network is designed. Finally, using this dataset, a new fuzzy logic controller is generated through a fuzzy neural network. Compared with traditional fuzzy logic controllers, data-driven controllers exhibit a more reasonable distribution of variable parameters, thus verifying the superiority of neural network-based controllers. Numerical simulations show that the proposed method improves both the path length and the navigation time, while ensuring safety in complex environments.
This paper proposes a data-driven fuzzy logic control method to improve the Dynamic Window Approach (DWA) for path planning of Unmanned Surface Vehicles (USVs). The proposed method aims to reduce the subjectivity introduced by human factors in the parameter settings of conventional fuzzy logic control. A data-driven dataset was constructed by extracting parameters from conventional fuzzy logic control algorithms, and a fuzzy neural network was employed to derive a new fuzzy logic controller from this dataset. The resulting controller exhibits a more rational distribution of variables compared to conventional fuzzy logic controllers, demonstrating the superiority of controllers generated by neural networks. Numerical simulations show that proposed the data-driven USVs path planning method offers improvements in terms of path length, navigation time, and reduced turning angles.
ArticleNumber 844
Author Wang, Yuanhui
Chemori, Ahmed
Zhang, Xiaoyue
Wang, Chenglong
Zhang, Kun
Zhang, Yuxuan
Wang, Feng
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  surname: Wang
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  givenname: Kun
  surname: Zhang
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  givenname: Yuxuan
  surname: Zhang
  fullname: Zhang, Yuxuan
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Issue 7
Keywords Fuzzy logic control
Path planning
Data-driven
Neural network
Dynamic Window Approach
Unmanned Surface Vehicles (USVs)
Language English
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– ident: 7318_CR18
  doi: 10.1109/ICRA48506.2021.9561462
– volume: 235
  start-page: 121240
  year: 2024
  ident: 7318_CR40
  publication-title: Expert Syst Appl
  doi: 10.1016/j.eswa.2023.121240
– ident: 7318_CR33
  doi: 10.1609/aaai.v35i12.17315
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Snippet The parameters of the path planning algorithm for unmanned surface vehicles (USVs) usually rely on manual settings, which makes it difficult for the algorithm...
This paper proposes a data-driven fuzzy logic control method to improve the Dynamic Window Approach (DWA) for path planning of Unmanned Surface Vehicles...
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StartPage 844
SubjectTerms Algorithms
Automatic
Automatic Control Engineering
Computer Science
Control algorithms
Control methods
Datasets
Engineering Sciences
Fuzzy control
Fuzzy logic
Kinematics
Neural networks
Parameters
Path planning
Robotics
Surface vehicles
Unmanned vehicles
Velocity
Title Data-driven fuzzy logic control method for improved USV path planning
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-05061978
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