Author:
R Hariharan,Saxena Archana,Dhote Vijay,S Srisathirapathy,Almusawi Muntather,Raja Kumar Jambi Ratna
Abstract
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. We 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.
Reference38 articles.
1. Power cognition: Enabling intelligent energy harvesting and resource allocation for solar-powered UAVs
2. Perpetual flight with a small solar-powered UAV: Flight results, performance analysis and model validation
3. Jurj S.L., Rotar R., Opritoiu F., Vladutiu M. (2020). Efficient Implementation of a Self-sufficient Solar-Powered Real-Time Deep Learning-Based System. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_7
4. Brooke-Holland L., in House of Commons Library, UK, 2012.
5. Arjomandi A., Agostino S., Mammone M., Nelson M., Zhou T., in Report for Mechanical Engineering Class, Adelaide, Australia 2006.