This paper introduces a comprehensive framework for generating obstacle-free flight paths for unmanned aerial vehicles (UAVs) in intricate 3D environments. The system leverages the Rapidly Exploring Random Tree (RRT) algorithm to design trajectories that effectively avoid collisions with structures of diverse shapes and sizes. Discussion revolves around the challenges encountered during development and the successful achievement of generating collision-free routes. While the system represents an initial iteration, it serves as a foundation for future projects aiming to refine and expand upon its capabilities. Future work includes simulation testing and integration into UAV missions for image acquisition and structure scanning. Additionally, considerations for swarm deployment and 3D reconstruction using various sensor combinations are outlined. This research contributes to the advancement of autonomous UAV navigation in real-world scenarios.

This study presents an innovative approach for the autonomous navigation of unmanned aerial vehicles (UAVs) in complex three-dimensional environments. By implementing the Rapidly Exploring Random Tree (RRT) algorithm, the system can efficiently plot safe flight paths that avoid obstacles of varying shapes and sizes. The discussion includes the technical obstacles faced and the strategies employed to overcome them, leading to the successful development of collision-free navigation routes. This foundational work aims to support future projects that will further refine the system through extensive simulations and real-world UAV deployments for tasks such as image capture and structural inspections.

Fraudulent online payment operations represent a persistent challenge in digital commerce, particularly in sectors like air travel, where credit and debit card payments dominate. This study presents a novel fraud detection framework tailored to airline ticket purchases, combining a synthetic dataset generator with a modular, customizable feature engineering process. These are two machine learning models—support vector machines (SVMs) and the light gradient boosting machine (LightGBM)—for real-time fraud detection. A synthetic dataset was generated, including a rich set of engineered features reflecting realistic user, transaction, and flight-related attributes. While both models were evaluated using classification-evaluation metrics, LightGBM outperformed SVMs in terms of overall performance with an accuracy of 94.2% and a recall of 71.3% for fraudulent cases. The main contribution of this study is the design of a reusable, customizable feature engineering framework for fraud detection in the airline sector, along with the development of a lightweight, adaptable fraud detection system for merchants, especially small and medium-sized enterprises. These findings support the use of advanced machine learning methods to enhance security in digital airline transactions.

Sustainability is one of the main concerns of the aviation industry nowadays, as it strives to reduce emissions and noise, both from flight operations and in the airport environment. Batteries are a promising technology for meeting these challenges. From a sustainable aviation perspective, they can be used as the main power source for all-electric aircraft, but also to support other technologies in hybrid-electric aircraft. To scale up this technology, it is of great interest to classify and evaluate the different battery options, not only currently available, but also with a view to future developments. This work presents the main keywords related to the use of batteries in aviation. It then explains the classification of batteries developed in the framework of the Environmentally Friendly Aviation for All Classes of Aircraft (EFACA) project, together with the main results of an analysis of each battery type in terms of its current status and its potential for future development.

This article proposes a systematic methodology for developing a certification standard for AI safety-critical systems in military aviation, combining military and civil airworthiness references. It involves a thorough analysis conducted to identify overlaps, contradictions, and specific needs for AI certification in this field. The methodology includes incremental updates to a foundational certification framework, continuously integrating new references. An illustrative application to an ISO reference demonstrates the process of extracting AI certification requirements, and systematically derived requirements from various ISO references exemplify the methodology’s efficacy. The aim of this approach is to consolidate pertinent information to establish robust certification standards, ensuring comprehensive coverage of relevant criteria.