A Review of Robotic Aircraft Skin Inspection: From Data Acquisition to Defect Analysis

Minnan Piao, Xuan Wang, Weiling Wang, Yonghui Xie and Biao Lu ()
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Minnan Piao: College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
Xuan Wang: College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
Weiling Wang: College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
Yonghui Xie: Intelligent Manufacturing Department, CISDI Information Technology Co., Ltd., Chongqing 401147, China
Biao Lu: Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300353, China

Mathematics, 2025, vol. 13, issue 19, 1-43

Abstract: In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) for external skin inspection, which present clear technical contributions, while excluding internal inspections and non-technical reports. Literature was retrieved from IEEE conferences, journals, and other academic databases, and key findings were summarized via the categorical analysis of motion planning, perception modules, and defect detection algorithms. Key limitations identified include the fragmentation of core technical modules, unresolved bottlenecks in dynamic environments, challenges in weak-texture and all-weather perception, and a lack of mature integrated systems with practical validation. The study concludes by advocating for future research in multi-robot heterogeneous collaborative systems, intelligent dynamic task scheduling, large model-based airworthiness assessment, and the expansion of inspection scenarios, all aimed at achieving fully autonomous and reliable operations.

Keywords: aircraft skin inspection; robotic inspection systems; multi-robot collaboration; coverage path planning; defect detection algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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