Azimuthal Solar Synchronization and Aerodynamic Neuro-Optimization: An Empirical Study on Slime-Mold-Inspired Neural Networks for Solar UAV Range Optimization
Journal
Applied Sciences
Date Issued
2024-09-13
Author(s)
Graheeth Hazare, Mohamed Thariq Hameed Sultan, Dariusz Mika, Farah Syazwani Shahar, Grzegorz Skorulski, Marek Nowakowski, Andriy Holovatyy, Ile Mircheski, Wojciech Giernacki
DOI
https://www.mdpi.com/2955316
Abstract
This study introduces a novel methodology for enhancing the efficiency of solar-powered unmanned aerial vehicles (UAVs) through azimuthal solar synchronization and aerodynamic neuro-optimization, leveraging the principles of slime mold neural networks. The objective is to broaden the operational capabilities of solar UAVs, enabling them to perform over extended ranges and in varied weather conditions. Our approach integrates a computational model of slime mold networks with a simulation environment to optimize both the solar energy collection and the aerodynamic performance of UAVs. Specifically, we focus on improving the UAVs’ aerodynamic efficiency in flight, aligning it with energy optimization strategies to ensure sustained operation. The findings demonstrated significant improvements in the UAVs’ range and weather resilience, thereby enhancing their utility for a variety of missions, including environmental monitoring and search and rescue operations. These advancements underscore the potential of integrating biomimicry and neural-network-based optimization in expanding the functional scope of solar UAVs.
Subjects