[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123060-en":3,"doc-seo-123060-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123060,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A convolutional neural network machine learning based navigation of underwater vehicles under limited communication","The paper addresses navigation and wide-area surveying for multiple autonomous underwater vehicles operating under low data rate and limited acoustic communication. Two AUVs move in formation using clustering and then select an optimal path whose performance is constrained by sparse observations. A machine-learning acoustical localization and communication (ML-ALOC) state compression method is proposed by approximating AUV states via hierarchical clustering followed by an optimal selection using convolutional neural networks. Extensive simulations compare the proposed ML-ALOC approach against a particle/K-means state compression scheme using AIC-driven selection.","10.24425/acs.2024.149671  \nArchives of Control Sciences Volume 34(LXX), 2024  \nNo. 3, pages 537–568  \nA convolutional neural network machine learning based navigation of underwater vehicles under limited communication  \nSarada Prasanna SAHOO, Bibhuti Bhusan PATI and Bikramaditya DAS   \nThis paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate. We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN) . The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.  \nKey words: Autonomous Underwater Vehicle (AUV), machine learning, hierarchical clustering, Convolutional Neural Network (CNN)  \nCopyright © 2024. The Author(s) . This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (CC BY-NC-ND 4.0 [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by-nc-nd/4.0/](by-nc-nd/4.0/)), which permits use, distribution, and reproduction in any medium, provided that the article is properly cited, the use is non-commercial, and no modifications or adaptations are made  \nS.P. Sahoo (e-mail: [sarada143@gmail.com](sarada143@gmail.com)) and B.B. Pati (e-mail: [bbpati_ee@vssut.ac.in](bbpati_ee@vssut.ac.in)) are with Department of Electrical Engineering, VSS University of Technology, Burla, India.  \nB. Das (corresponding author, e-mail: [adibik09@gmail.com](adibik09@gmail.com)) is with Department of Electronics and Telecommunication Engineering, VSSUT Burla, Odisha, India but now joined as Associate Professor in Department of Electronics and Communication Engineering, CUPGS, BPUT, Rourkela, Odisha, India.  \nThis work is supported by IIT Guwahati Technology Innovation and Development Foundation (IITGTI&DF), which has been set up at IIT Guwahati as a part of the National Mission on Interdisciplinary Cyber Physical Systems (NMICPS), with the financial assistance from Department of Science and Technology, India through grant number DST/NMICPS/TIH12/IITG/2020 . Authors gratefully acknowledge the support provided for the present work.  \nReceived 28 .10.2023. Revised 16 .6.2024.  \n1. Introduction  \nAUTONOMOUS underwater vehicles (AUVs) can be deployed in selforganizing tasks in various applications such as marine climate perceptions, exploring benthic resources, studying underwater terrain features and monitoring underwater life, military fields [1–5] . Self-localizing with multiple AUVs is preferred to exhibit ocean floor survey missions and deep-sea mapping operation [6] . Underwater mission planning with multiple AUVs needs cooperative path planning to reduce time and energy costs. These missions are suitable for observing targets using positioning references and limited data processing capabilities in uncertain ocean environment. Cooperative path planning control is a difficult task to achieve due to the uncertainties in AUV dynamics, underwater environment and limi","cbCaifth415iWxHb","https://ap.wps.com/l/cbCaifth415iWxHb","pdf",7054815,1,32,"English","en",105,"# Introduction\n## Problem background: limited communication and localization\n## Related approaches: particle filtering, clustering, and estimation\n## Motivation: state compression and optimal path planning","[{\"question\":\"What problem does the paper target in multi-AUV navigation?\",\"answer\":\"It targets wide-area navigation for multiple autonomous underwater vehicles where low data rate and limited acoustic communication reduce localization quality and complicate cooperative path planning.\"},{\"question\":\"How does ML-ALOC improve state compression under limited communication?\",\"answer\":\"It compresses AUV state information using hierarchical clustering for approximation, then applies convolutional neural networks to select an optimal state using an AIC-based decision process.\"},{\"question\":\"How is the proposed method evaluated compared with existing approaches?\",\"answer\":\"The paper conducts extensive simulations with two AUVs following trajectories, comparing ML-ALOC against a particle state compression method based on K-means clustering at each iteration with AIC-based selection.\"}]","A convolutional neural network machine learning based navigation of underwater vehicles under limited communication | 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problem does the paper target in multi-AUV navigation?","Question",{"text":75,"@type":76},"It targets wide-area navigation for multiple autonomous underwater vehicles where low data rate and limited acoustic communication reduce localization quality and complicate cooperative path planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does ML-ALOC improve state compression under limited communication?",{"text":80,"@type":76},"It compresses AUV state information using hierarchical clustering for approximation, then applies convolutional neural networks to select an optimal state using an AIC-based decision process.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed method evaluated compared with existing approaches?",{"text":84,"@type":76},"The paper conducts extensive simulations with two AUVs following trajectories, comparing ML-ALOC against a particle state compression method based on K-means clustering at each iteration with AIC-based 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