In-Flight Energy-Driven Composition of Drone
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Date
2023-09
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
This project explores the innovative concept of In-Flight Energy-Driven Composition of
Drones to improve the capabilities and sustainability of unmanned aerial vehicles (UAVs).
By harnessing renewable energy sources such as solar cells and using genetic algorithms in
Python, the goal is to optimize the in-flight composition of drones, resulting in longer
operational life, lower carbon emissions, and increased payload capacity.
Description
Keywords
UAVs (Unmanned Aerial Vehicles), Genetic Algorithms, MATHEMATICS::Applied mathematics::Optimization, systems theory