Modern Approaches To Optimizing Technical Documentation: challenges and solutions for unmanned aircraft systems

Abstract

This article discusses the potential of automation in restructuring the technical documentation required for certification of unmanned aircraft systems (UAS). Certification is a critical regulatory process to ensure compliance with national and international safety standards. Preparing technical documentation for certification is time-consuming, error-prone, and costly process, with manual methods significantly reducing efficiency. These shortcomings pose challenges for mass UAS certification, which is becoming increasingly relevant due to the rapid growth of the industry. This article reviews current technologies that can be used to address these challenges, including machine learning, natural language processing (NLP), knowledge graphs, and semantic analysis. Tools such as EasyOCR, OnToCode, and advanced models such as BERT are reviewed in terms of their suitability for text recognition, data mining, and automatic restructuring of complex technical documents. In addition, the study provides examples of possible practical applications of these methods, focusing on their feasibility to standardise document preparation, reduce human errors and support mass certification of UAS. Challenges such as lack of harmonisation of document formats, integration of legacy systems, and regulatory dynamism are also discussed. The study concludes that despite the emerging opportunities for automation to optimise certification processes, its practical implementation requires further study and collaboration with regulators. The proposed review serves as a conceptual framework that highlights the potential benefits and limitations of integrating automation into the UAS certification workflows and paves the way for future advances in this critical area.