[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127030-en":3,"doc-seo-127030-105":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},127030,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Scienti􀀂c Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics","Develops an AI-aided tactics generator for uncrewed surface vessels to defend critical infrastructure and maritime assets against surface vehicle attacks. The proposed SciML methodology embeds physical principles into the learning process, improving generalization and accuracy in conditions not seen during training. The work targets a key limitation of existing maritime AI approaches: effective operation under novel or changing environments without requiring costly retraining. ","2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC), 29 September-3 October 2024, San Diego, CA, USA  \nDOI:10.1109/DASC62030.2024.10749622  \nScienti􀀂c Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense  \nTactics  \nUgurcan Celik, Mevlut Uzun, Gokhan Inalhan  \nAutonomy and AI, Cran􀀂eld University Cran􀀂eld, United Kingdom  \nEmail : {ugurcan.celik, mevlut.uzun, inalhan}@cran􀀂eld.ac.uk  \nMike Woods  \nBAE Systems Maritime Portsmouth, United Kingdom [mike.woods@baesystems.com](mike.woods@baesystems.com)  \nAbstract—In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scienti􀀂c machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model’s ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.  \nIndex Terms—autonomous decision making, maritime defence, pursuit evasion, scienti􀀂c machine learning  \nI. INTRODUCTION  \nThe need for advanced security measures to protect critical infrastructure and maritime assets has revealed the inadequacies of traditional defense approaches in the marine environment. Integrating arti􀀂cial intelligence (AI) and autonomous systems into Unmanned Surface Vehicle (USV) operations is essential to enhance the effectiveness and adaptability of maritime security strategies. These technologies signi􀀂cantly improve the strategic capabilities of USVs. AI-aided tactics for infrastructure protection using USVs are a critical application of these technologies, effectively modeled as a pursuit-evasion game where the USV is the pursuer and an intruder vessel is the evader. Although pursuit-evasion strategies are still in their early stages of application for USVs, they have been extensively explored in other domains. Research in this area typically divides methods into two categories: conventional control techniques and machine learning approaches.  \nThe conventional approach involves applying classical control and optimization theory. One such methodology is the use of the Model Predictive Control framework to design a controller [1] . A study in [2] utilized MPC for the USV pursuit and evasion game scenario and similar applications have been made in other robotic platforms [3], [4] . However, implementing the MPC methods in pursuit-evasion games necessitates numerous simpli􀀂cations, resulting in diminished performance in real-world applications.  \nAnother method in the 􀀂rst category applies game theory, where the problem is treated as a min-max optimization  \nproblem and addressed based on cost de􀀂nitions [5] . Pursuitevasion, a subtopic of game theory, is extensively studied across various application areas. For maritime domain, the study in [6] analyzes how game theory is applied to various USV tasks. In [7], the authors investigated the pursuitevasion game problem for USVs based on the threat potential 􀀂eld (TPF) . Similarly, game theory methodology has been investigated and applied for other platforms like robotics, unmanned aerial vehicles and unmanned underwater vehicles. Rajan [et. al. studied](et. al. studied) the pursuit-evasion of two aircraft in a horizontal planed and proposed a solution based on extremal trajectory maps [8] . Another study in [9] solved the problem through modelling the aircraft control input limits, creating the Hamiltonian equation and solving the optimization problem numerically. The authors of [10] presented a multiplayer pursuit-evasion game consisting of prey, predator, and protector for collision avoidance problem. Gong [et. al. handled](et. al. handled)[ ](et. al. handled)[the pursuit-evasion problem for tw","cbCaiq2IlTYGHLVy","https://ap.wps.com/l/cbCaiq2IlTYGHLVy","pdf",863527,1,12,"English","en",105,"# Introduction\n## Conventional control and game-theoretic methods\n## Machine learning and reinforcement learning approaches\n## Motivation and problem framing","[{\"question\":\"What problem does the document address for uncrewed surface vessels?\",\"answer\":\"It addresses designing tactics for USVs to protect critical national infrastructure and maritime assets when facing surface vehicle attacks, framed as a pursuit-evasion game.\"},{\"question\":\"How does the proposed method improve generalization compared with typical AI approaches?\",\"answer\":\"By using a SciML methodology that incorporates physical principles into training, the approach improves performance in scenarios not encountered during training.\"},{\"question\":\"Why is retraining often avoided in the document’s proposed approach?\",\"answer\":\"Because the innovation aims to handle novel or changing conditions effectively without needing retraining, addressing a key gap in existing maritime AI applications.\"}]","Scienti􀀂c Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics | 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