[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119069-en":3,"doc-seo-119069-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119069,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Advancements in Solar-Powered UAV Design Leveraging Machine Learning - A Comprehensive Review","Unmanned Aerial Vehicles (UAVs) have advanced through the integration of solar power and machine learning, enabling improved endurance and smarter onboard decision-making. This comprehensive review covers solar-powered UAV design principles, energy-harvesting technologies, and system-level considerations for different application domains such as environmental monitoring and agriculture. It highlights machine learning’s role in optimizing performance through intelligent energy harvesting and resource allocation using reinforcement learning, and discusses evaluation results, challenges, and future directions including distributed learning and continuous 3D decision making.","Advancements in Solar-Powered UAV Design Leveraging Machine Learning: A Comprehensive Review  \nHariharan R1, Archana Saxena2, Vijay Dhote3, Srisathirapathy S4, Muntather Almusawi5, and Dr. Jambi Ratna Raja Kumar6  \n1Assistant Professor, School of Business and Management, CHRIST (Deemed to be University),Bangalore Yeshwantpur Campus India.  \n2Department of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun, Uttarakhand, India.  \n3Department of Computer Science & Engineering, IES College of Technology,IES University, Bhopal, Madhya Pradesh 462044 India.  \n4Assistant Professor, Department of MECH, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127, India.  \n5The Islamic university, Najaf, Iraq.  \n6Associate Professor, Department of Computer Engineering, Genba Sopanrao Moze College of Engineering, Balewadi, Pune, Maharashtra, India Email: [ratnaraj.jambi@gmail.com](ratnaraj.jambi@gmail.com).  \nAbstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques.  \nWe delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology.  \nKeywords: Resource, NAV, Rotor-driven, controllers, SUAV, flight endurance.  \n[1](1hari712@gmail.com)[hari712@gmail.com](1hari712@gmail.com)  \n[2](2dr12archana@gmail.com)[dr12archana@gmail.com](2dr12archana@gmail.com)  \n[3](3research@iesbpl.ac.in)[research@iesbpl.ac.in](3research@iesbpl.ac.in)  \n[4](4S.SRISATHIRAPATHY_mech@psvpec.in)[S.SRISATHIRAPATHY_mech@psvpec.in](4S.SRISATHIRAPATHY_mech@psvpec.in)  \n[5](5muntatheralmusawi@gmail.com)[muntatheralmusawi@gmail.com](5muntatheralmusawi@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nIntroduction  \nSolar-powered UAVs represent a compelling convergence of clean energy and autonomous flight capabilities. This review explores the intersection of solar power and machine learning in the context of UAVs. We discuss the potential of this integration and its implications for various industries, including environmental monitoring, agriculture, surveillance, and more. The article introduces a new innovative method called power cognition, which focuses on intelligent energy harvesting and resource allocation to tackle the resource management issues faced by solar-powered unmanned aerial vehicles (SUAVs) . The power cognition scheme relies on the reinforcement learning (RL) mechanism, allowing for a collective optimization of flight trajectory, energy harvesting, and information transmission through an intelligent self-learning process. The article evaluates the performance of the power cognitive SUAVs using simulation [1] . The findings indicate that the suggested approach, which incorporates a significant discount factor, delivers optimal throughput and energy efficiency. The SUAV determines the best course of action for the next state by considering the energy arrivals expected in the future. The article concludes by discussing the future work, which includes extending the work to distributed learning for multi-SUAV networks and considering the SUAV decision-making in continuous three-dimensional space [","cbCaitk00ukJzBmS","https://ap.wps.com/l/cbCaitk00ukJzBmS","pdf",1990331,1,12,"English","en",105,"# Introduction\n## Power cognition and reinforcement learning optimization\n## Solar-powered UAV achievements and prior deep learning work\n# Design Principles of Solar-Powered UAVs\n## Classification of UAVs by size\n# Features of AtlantikSolar UAV","[{\"question\":\"What is the main focus of the review on solar-powered UAVs?\",\"answer\":\"The review examines how solar power and machine learning are combined to enhance UAV capabilities, emphasizing design approaches, energy harvesting, and performance optimization across applications.\"},{\"question\":\"What is power cognition in the context of SUAVs?\",\"answer\":\"Power cognition is an intelligent scheme for energy harvesting and resource allocation that uses reinforcement learning to collectively optimize flight trajectory, energy harvesting, and information transmission.\"},{\"question\":\"How is the proposed power cognition approach evaluated and what are the results?\",\"answer\":\"The approach is evaluated via simulation of power-cognitive SUAVs, and the findings indicate optimal throughput and energy efficiency attributed to the use of a significant discount factor for future expected energy arrivals.\"}]","Advancements in Solar-Powered UAV Design Leveraging Machine Learning - 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