Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network
This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detecti...
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| Published in | Information sciences Vol. 673; p. 120684 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2024.120684 |
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| Abstract | This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency. |
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| AbstractList | This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency. |
| ArticleNumber | 120684 |
| Author | Sun, Qian Huang, Yujie Zhang, Zitang Yu, Qiang Zou, Zili Li, Yibing |
| Author_xml | – sequence: 1 givenname: Yibing surname: Li fullname: Li, Yibing organization: College of Information and Communication, Harbin Engineering University, Harbin, 150001, China – sequence: 2 givenname: Yujie surname: Huang fullname: Huang, Yujie organization: College of Information and Communication, Harbin Engineering University, Harbin, 150001, China – sequence: 3 givenname: Zili surname: Zou fullname: Zou, Zili organization: College of Information and Communication, Harbin Engineering University, Harbin, 150001, China – sequence: 4 givenname: Qiang surname: Yu fullname: Yu, Qiang organization: Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao, 266000, China – sequence: 5 givenname: Zitang surname: Zhang fullname: Zhang, Zitang organization: College of Information and Communication, Harbin Engineering University, Harbin, 150001, China – sequence: 6 givenname: Qian orcidid: 0000-0001-7267-7545 surname: Sun fullname: Sun, Qian email: qsun@hrbeu.edu.cn organization: College of Information and Communication, Harbin Engineering University, Harbin, 150001, China |
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| Keywords | Consensus-based bundle algorithm Glasius bio-inspired neural network Static target search Coverage path planning Autonomous underwater vehicle |
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| SubjectTerms | Autonomous underwater vehicle Consensus-based bundle algorithm Coverage path planning Glasius bio-inspired neural network Static target search |
| Title | Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network |
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