Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

Abstract

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

Web and MATLAB-Based Platform for UAV Flight Management and Multispectral Image Processing

“…Another consideration is related to integrate predictive models based on the historical indices obtained for a given field to predict the evolution of the crop. Finally, it is also under consideration how to include within Web and MATLAB-based framework the "MATLAB UAV toolbox" for creating an optimal ecosystem for UAV fleets management, where some ideas are already undertaken in [41].…”

DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety

“…The authors [14] deployed drones for mapping raised bog vegetation communities using machine learning and deep learning classifiers. In another related work, Sergio et al [15] proposed a remote management architecture solar power plants surveillance using a fleet of UAVs. Though these works provided an invaluable insight into surveillance, it focused on using drones to detect objects rather than detecting drones and cannot perform automatic simultaneous detection and neutralization, as it requires a human expert's assistance.…”

Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management

“…One of the most challenging problems in traffic control is complex maneuvers, such as at traffic roundabouts and intersections [5,6]. UAVs can be used to cooperate with autonomous vehicles [7] or traffic infrastructures [8] in these complex maneuvers, providing them with information that would not be available without such aerial vision [9].…”