@inbook{doi:10.1049/PBPO263E_ch20,
author = {Eduardo Feo Flushing and Gianni A. Di Caro },
title = {AI and robotic techniques for PV inspection},
booktitle = {AI and Digitalization in Energy Management},
chapter = {Chapter 20},
pages = {407-432},
doi = {10.1049/PBPO263E_ch20},
URL = {https://digital-library.theiet.org/doi/abs/10.1049/PBPO263E_ch20},
eprint = {https://digital-library.theiet.org/doi/pdf/10.1049/PBPO263E_ch20},
abstract = { This chapter presented a systematic view of the application of AI and robotics in PV inspection, focusing on the key tasks involved - sensor selection, data collection strategies, and data processing. By structuring the discussion around these fundamental components, we provided a coherent framework highlighting both the technological advancements and the practical challenges faced in real-world implementations. Integrating AI and robotics has proven to be a powerful approach for addressing the limitations of traditional inspection methods, enabling more accurate, scalable, and efficient monitoring of PV systems. The application of AI and robotics for PV inspection is becoming an increasingly active area of research and development. Numerous survey papers are published each year, each with a distinct focus, reflecting the rapid advancements and growing interest in this field. However, while these surveys provide valuable insights into state-of-the-art techniques, there remains a gap between theoretical advancements and practical deployment. This chapter aimed to bridge that gap by focusing on the practitioner's perspective, emphasizing the technical challenges that arise in real-world applications, and discussing how AI and robotics can be effectively implemented in PV inspections. Despite the progress, significant challenges remain. Ensuring the reliability and robustness of AI-driven inspection systems, addressing data limitations, improving real-time processing capabilities, and optimizing robotic autonomy for large-scale PV farms are critical issues that need further research. The chapter underscored these challenges, highlighting the need for continued innovation in multi-modal data fusion, adaptive AI models, and collaborative robotic inspection strategies. By presenting a structured and application-oriented overview, this chapter serves as a resource for researchers and practitioners looking to implement AI and robotics in PV inspection. As this field continues to evolve, bridging the gap between theoretical advancements and practical deployment will be crucial in realizing the full potential of these technologies in ensuring the efficiency, reliability, and sustainability of solar energy systems. }
}
