Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

• Published in: Information sciences Vol. 673; p. 120684
• Main Authors: Li, Yibing, Huang, Yujie, Zou, Zili, Yu, Qiang, Zhang, Zitang, Sun, Qian
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2024
• Subjects: Autonomous underwater vehicle; Consensus-based bundle algorithm; Coverage path planning; Glasius bio-inspired neural network; Static target search; Consensus-based bundle algorithm; Glasius bio-inspired neural network; Static target search; Coverage path planning; Autonomous underwater vehicle
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120684

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.