A real time data-driven dynamic glasius bionic neural network path planning algorithm for polar under-ice feature scanning by “Xinghai 1000” AUV

• Published in: Ocean engineering Vol. 312; p. 119289
• Main Authors: Zhou, Lin, Deng, Zhongchao, Bai, Guiqiang, Zhou, Nan, Qin, Hongde, Zhu, Zhongben, Xue, Yifan, Mu, Xiaokai, Cao, Xiaojian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.11.2024
• Subjects: Complete coverage path planning; Neural network algorithm; Polar under-ice scanning; Under-ice experiment; Xinghai 1000; Neural network algorithm; Xinghai 1000; Complete coverage path planning; Under-ice experiment; Polar under-ice scanning
• ISSN: 0029-8018
• DOI: 10.1016/j.oceaneng.2024.119289

Using Autonomous Underwater Vehicle (AUV) to survey polar under-ice feature is crucial for studying polar marine environment and global climate change. Compared with seafloor terrain scanning, it faces several challenges, such as uncertain under-ice environment, lack of reliable positioning methods, and stringent constraints on recovery location. Thus, taking “Xinghai 1000” as analysis object, this paper proposes a real time data-driven dynamic glasius bionic neural network path planning algorithm (RDGPP), which consists of an online bi-level path planning module. In global planning module, dynamically hierarchical strategy is used to realize task area layering. On this basis, global coverage path is planned by combining neuron activity value updated which utilize real-time environmental information obtained by FLS sonar with multivariant strategy. Moreover, due to AUV recovery mode limitation, return strategy has been devised in multivariant strategy to ensure AUV automatic return to launch point, thereby reducing energy usage. In local planning module, Flagged bit and GBNN-driven A-star algorithm (FGA) is introduced to minimize deadlock escape time for AUV. Simulation experiments verify RDGPP algorithm advancement in key indicators such as turning times, running time, etc., and effectiveness is verified by “Xinghai 1000” under-ice experiment. •A real time data-driven dynamic glasius bionic neural network path planning algorithm for Polar AUV.•A dynamic hierarchical scanning method is proposed to reduce task scanning area and energy consumption.•A real time environmental information combined with multivariate strategy to global coverage path planning.•An improved algorithm based on activity value is designed to online plan dead zone escape path.