The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPC

翻译：光伏产业正经历向大型太阳能电站的显著转型，传统检测方法已被证明耗时且成本高昂。当前，基于无人机的光伏检测主流方法依赖摄影测量技术，但该技术存在飞行过程中无效数据增多、图像分辨率问题以及高空飞行检测过程受限等缺陷。本研究开发了一种具有动态补偿能力的无人机视觉伺服控制系统，采用非线性模型预测控制算法，能够在不同前进速度与高度约束条件下精确跟踪光伏阵列中线，确保低空飞行时获取高分辨率图像。该视觉伺服控制器基于RGB-D图像特征提取与卡尔曼滤波算法，用于估算光伏阵列边缘。此外，本研究在仿真环境与真实场景中采用商用飞行器DJI Matrice 100进行实验验证，以展示架构性能。相关成果已开源至：https://github.com/EPVelasco/VisualServoing_NMPC