ABSTRACT

Artificial intelligence (AI) is undergoing a ground-breaking period. Recently, AI affects almost every part of human life. Using AI in path planning for Unmanned Aerial Vehicle (UAV) attracts attention as a novel need. The inputs that form the base of UAV use in photogrammetry are UAV Type (UT), Ground Sampling Distance (GSD), Overlap Rates (OR), and Atmospheric Conditions (AC). Input parameters directly impact the UAV’s Flight Time (FT) and Battery Status (BS). Weighting and optimizing these parameters are the main ideas of this study.

The effects of input values (GSD, OR, UT, AC) on the outputs (BS and FT) were optimized using Artificial Neural Networks (ANN) in this study. For the analysis, results have been produced in which different training algorithms are preferred (Gradient Descent – GD – and Levenberg-Marquardt – LM). The GD algorithm has reached 77.65% accuracy in FT estimation and 80.91% estimation accuracy on normalized data on the BS. Then, the correlation between the produced model and the input parameters and the output parameters was determined, and the weights of the inputs were revealed. As a result, it was determined that the AC parameter has the most significant effect on BS and FT. Also, it has been identified that the normalization process has a considerable impact on optimization.

摘要

人工智能 (AI) 正处于开创性时期。最近，人工智能几乎影响了人类生活的方方面面。在无人驾驶飞行器 (UAV) 的路径规划中使用 AI 作为一种新需求引起了人们的关注。构成 UAV 在摄影测量中使用基础的输入是 UAV 类型 (UT)、地面采样距离 (GSD)、重叠率 (OR) 和大气条件 (AC)。输入参数直接影响无人机的飞行时间 (FT) 和电池状态 (BS)。对这些参数进行加权和优化是本研究的主要思想。

在本研究中，使用人工神经网络 (ANN) 优化了输入值（GSD、OR、UT、AC）对输出（BS 和 FT）的影响。对于分析，已经产生了首选不同训练算法的结果（梯度下降 – GD – 和 Levenberg-Marquardt – LM）。GD 算法在 FT 估计中达到了 77.65% 的准确率，在 BS 上的归一化数据上达到了 80.91% 的估计准确率。然后，确定生成的模型与输入参数和输出参数之间的相关性，并揭示输入的权重。结果，确定 AC 参数对 BS 和 FT 的影响最显着。此外，已经确定归一化过程对优化有相当大的影响。