Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2950
Title: Estimation of UAV flight time and Battery Consumption for photogrammetric application using multiple machine learning algorithms
Authors: Bilgehan, Makineci Hasan
Mustafa, Hüsrevoğlu
Hakan, Karabörk
Keywords: ANFIS
ANN
battery consume estimation
flight time estimation
performance evaluation
PSO-FIS
UAV
Antennas
Fuzzy neural networks
Learning algorithms
Machine learning
Particle swarm optimization (PSO)
Secondary batteries
Adaptive network-based fuzzy inference system
Adaptive-network- based fuzzy inference systems
Aerial vehicle
Battery consume estimation
Flight time
Flight time estimation
Fuzzy inference systems
Particle swarm
Particle swarm optimization-fuzzy inference system
Performances evaluation
Swarm optimization
Time estimation
Unmanned aerial vehicle
Fuzzy inference
Issue Date: 2022
Publisher: Institute of Physics
Abstract: In recent years, important research has been conducted in Machine Learning (ML), especially on Artificial Neural Networks (ANN). Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization-Fuzzy Inference System (PSO-FIS) algorithms are popular ML algorithms like ANN. In terms of their working architecture and results, ANN, ANFIS, and PSO-FIS algorithms can obtain useful solutions for different nonlinear problems. This study evaluated the performance of the ANN, ANFIS, and PSO-FIS algorithms and compared the estimation results. Regarding the application, the test and target data was obtained from the flights performed with Unmanned Aerial Vehicles (UAV), including how long the UAV operates (i.e., Flight Time, FT) and how much battery the UAV consumes during the flight (i.e., Battery Consumption, BC). To obtain FT and BC outputs, sixty-five pre- and post-flight data tables were created. The best iterations for estimating the outputs using the three ML algorithms (considering the minimum/maximum values, RMSE, R, and R2) were determined and discussed based on the training, validation, and test estimations. © 2022 IOP Publishing Ltd.
URI: https://doi.org/10.1088/2631-8695/ac7a0b
https://hdl.handle.net/20.500.13091/2950
ISSN: 2631-8695
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

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