Scholarlink

<p>This paper presents a novel framework for collaborative inspection of solar panel farms that use the complementary capabilities of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The UGV, equipped with a YOLOv5-based computer vision system, performs cable detection and ground-level navigation. At the same time, the UAV focuses on aerial inspection tasks such as identifying surface-level anomalies in solar panels. The proposed system integrates real-time data exchange, path planning, and a cognitive framework with a human-in-the-loop mechanism to improve the decision-making and adaptability of the systems. Simulations conducted in the Robot Operating System (ROS) and Gazebo validate the framework’s performance in addressing operational challenges such as collaborative task execution and autonomous decision-making.</p>

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Collaborative Inspection of Solar Panel Farms Using YOLOv5-Based Computer Vision and UGV-UAV Integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Johann S. J. C. Amorim,
  • Accacio F. S. Neto,
  • Rafael S. Chaves,
  • Alessandro R. L. Zachi,
  • Josiel A. Gouvêa,
  • Fabio A. A. Andrade,
  • Milena F. Pinto

摘要

This paper presents a novel framework for collaborative inspection of solar panel farms that use the complementary capabilities of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs). The UGV, equipped with a YOLOv5-based computer vision system, performs cable detection and ground-level navigation. At the same time, the UAV focuses on aerial inspection tasks such as identifying surface-level anomalies in solar panels. The proposed system integrates real-time data exchange, path planning, and a cognitive framework with a human-in-the-loop mechanism to improve the decision-making and adaptability of the systems. Simulations conducted in the Robot Operating System (ROS) and Gazebo validate the framework’s performance in addressing operational challenges such as collaborative task execution and autonomous decision-making.