Browsing by Author "Plastropoulos, Angelos"

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    Advancements in 3D x-ray imaging: development and application of a twin robot system
    (Springer, 2024-08-31) Asif, Seemal; Hryshchenko Sumina, Yuliya; Holden, Martin; Contino, Matteo; Adiuku, Ndidiamaka; Hughes, Bryn; Plastropoulos, Angelos; Avdelidis, Nico; Webb, Phil
    development of a novel twin robot system for 3D X-ray imaging integrates advanced robotic control with mobile X-ray technology to significantly enhance diagnostic accuracy and efficiency in both medical and industrial applications. Key technical aspects, including innovative design specifications and system architecture, are discussed in detail. The twin robots operate in tandem, providing comprehensive imaging capabilities with high precision. This novel approach offers potential applications ranging from medical diagnostics to industrial inspections, significantly improving over traditional imaging methods. Preliminary results demonstrate the system's effectiveness in producing detailed 3D images, underscoring its potential for wide-ranging uses. Future research will focus on optimizing image quality and automating the imaging process to increase utility and efficiency. This development signifies a step forward in integrating robotics and imaging technology, promising enhanced outcomes in various fields.
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    Advancements in learning-based navigation systems for robotic applications in MRO hangar: review
    (MDPI, 2024-02-21) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos
    The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.
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    Aircraft skin machine learning-based defect detection and size estimation in visual inspections
    (MDPI , 2024-09-10) Plastropoulos, Angelos; Bardis, Kostas; Yazigi, George; Avdelidis, Nicolas P.; Droznika, Mark
    Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges.
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    CNN-fusion architecture with visual and thermographic images for object detection
    (SPIE, 2023-06-12) Adiuku, Amaka; Avdelidis, Nicolas Peter; Tang, Gilbert; Plastropoulos, Angelos; Perinpanayagam, Suresh
    Mobile robots performing aircraft visual inspection play a vital role in the future automated aircraft maintenance, repair and overhaul (MRO) operations. Autonomous navigation requires understanding the surroundings to automate and enhance the visual inspection process. The current state of neural network (NN) based obstacle detection and collision avoidance techniques are suitable for well-structured objects. However, their ability to distinguish between solid obstacles and low-density moving objects is limited, and their performance degrades in low-light scenarios. Thermal images can be used to complement the low-light visual image limitations in many applications, including inspections. This work proposes a Convolutional Neural Network (CNN) fusion architecture that enables the adaptive fusion of visual and thermographic images. The aim is to enhance autonomous robotic systems’ perception and collision avoidance in dynamic environments. The model has been tested with RGB and thermographic images acquired in Cranfield’s University hangar, which hosts a Boeing 737-400 and TUI hangar. The experimental results prove that the fusion-based CNN framework increases object detection accuracy compared to conventional models.
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    Digital Twin-Based Health Management for Complex Aircraft Systems: Case Studies and Applications
    (IEEE, 2025-03-24) Wang, Chengwei; Fan, Ip-Shing; Plastropoulos, Angelos
    Digital Twin technology, initially conceptualized during the NASA's Apollo program, has evolved into a transformative tool for system health management, particularly in aviation. By integrating high-fidelity simulations, real-time sensor data, and predictive analytics, DTs enable significant innovation in Prognostics and Health Management methods. This paper explores the application of DTs in health management for complex aircraft systems, focusing on two critical subsystems: Flight Control Electrical Actuators and Main Landing Gear. Leveraging MATLAB Simscape, modular DT frameworks were developed to simulate these systems under nominal, degraded, and fault conditions. The inclusion of fault injection models enables the generation of realistic datasets to support predictive maintenance, alleviating difficulties in data availability. Two case studies are presented to illustrate the potential of DT-based approaches to reduce downtime, optimizing performance, and enhancing system reliability. This paper provides a comparative analysis of existing DT tools, highlighting their capabilities and limitations in aerospace contexts. While platforms such as MATLAB Simulink and ANSYS Twin Builder offer robust modeling capabilities, operational tools like AVIATAR and IBM Maximo excel in fleet management and predictive analytics. This comparison highlights the need for tailored DT solutions that balance real-time capabilities, scalability, and configurability. This study contributes to the growing body of knowledge on DT technology, offering insights into its role in enhancing aviation safety, efficiency, and sustainability. It serves as a guide for applying DT-based health management, paving the way for broader adoption in next-generation aerospace systems.
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    Fusion and comparison of prognostic models for remaining useful life of aircraft systems
    (PHM Society, 2023-10-26) Fu, Shuai; Avdelidis, Nicolas P.; Plastropoulos, Angelos; Fan, Ip-Shing
    Changes in the performance of an aircraft system will straightforwardly affect the safe operation of the aircraft, and the technical requirements of Prognostics and Health Management (PHM) are highly relevant. Remaining Useful Life (RUL) prediction, part of the core technologies of PHM, is a cutting-edge innovation being worked on lately and an effective means to advance the change of upkeep support mode and work on the framework's security, unwavering quality, and economic reasonableness. This paper summarizes a detailed preliminary literature review and comparison of different prognostic approaches and the forecasting methods' taxonomy, the methodology's details, and provides its application to aircraft systems. It also provides a brief introduction to the predictive maintenance concept and condition-based maintenance (CBM). This article uses several predictive models to predict RUL and classifies conventional regression algorithms according to the similarity in function and form of the algorithms. More classical algorithms in each category are selected to compare the prediction results, and finally, the combined effects of the RUL prediction are obtained by weighted fusion, accuracy, and compatibility. The performance of the proposed models is assessed based on evaluations of RUL acquired from the hybrid and individual predictive models. This correlation depends on the most current prognostic metrics. The outcomes show that the proposed strategy develops precision, robustness, and adaptability. Hence, the work in this paper shall enrich the advancement of predictive maintenance and modern innovation of prognostic development.
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    The ‘hangar of the future’ for sustainable aviation
    (Cambridge University Press (CUP), 2024-11) Plastropoulos, Angelos; Fan, Ip-Shing; Avdelidis, Nicolas P.; Angus, Jim; Maggiore, John; Atkinson, Helen
    Sustainability is becoming a major strategic driver within the aviation industry, which has moved from providing primarily economic benefits to delivering the ‘triple bottom line’, including social and environmental impact as well as financial performance. Sustainable aviation is also being tracked by the International Civil Aviation Organisation (ICAO) Global Collation for Sustainable Aviation. Operations and Infrastructure is an important near-term opportunity to deliver sustainability benefits. Digital Technologies, Integrated Vehicle Health Management (IVHM) and Maintenance Repair and Overhaul (MRO) play a prominent role in implementing these benefits, with a particular focus on operational efficiencies. As part of this, the sustainable smart hangar of the future is a concept that is becoming more and more important in forming the future of the aviation industry. The Hangar of the Future is an excellent opportunity for innovation, combining the progress in manufacturing, materials, robotics and artificial intelligence technologies. Succeeding in developing a hangar with these characteristics will provide us with potential benefits ranging from reduced downtime and costs to improved safety and environmental impact. This work explores some of the key features related to the sustainable smart hangar of the future by discussing research that takes place in DARTeC’s (Digital Aviation Research and Technology Centre) hangar led by the IVHM Centre in Cranfield. Additionally, the paper touches on some longer-term aspirations.
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    Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach)
    (MDPI, 2024-04-02) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos
    The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.
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    Mobile robot obstacle detection and avoidance with NAV-YOLO
    (EJournal Publishing, 2024-03-22) Adiuku, Ndidiamaka; Avdelidis, Nicolas P.; Tang, Gilbert; Plastropoulos, Angelos; Diallo, Yanis
    Intelligent robotics is gaining significance in Maintenance, Repair, and Overhaul (MRO) hangar operations, where mobile robots navigate complex and dynamic environments for Aircraft visual inspection. Aircraft hangars are usually busy and changing, with objects of varying shapes and sizes presenting harsh obstacles and conditions that can lead to potential collisions and safety hazards. This makes Obstacle detection and avoidance critical for safe and efficient robot navigation tasks. Conventional methods have been applied with computational issues, while learning-based approaches are limited in detection accuracy. This paper proposes a vision-based navigation model that integrates a pre-trained Yolov5 object detection model into a Robot Operating System (ROS) navigation stack to optimise obstacle detection and avoidance in a complex environment. The experiment is validated and evaluated in ROS-Gazebo simulation and turtlebot3 waffle-pi robot platform. The results showed that the robot can increasingly detect and avoid obstacles without colliding while navigating through different checkpoints to the target location.
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    Wireless data transfer system architecture for predictive maintenance of aircraft landing gear
    (AIAA, 2025-01-06) Skaltsis, Georgios Marios; Wang, Chengwei; Plastropoulos, Angelos; Fan, Ip-Shing; Avdelidis, Nicolas P.
    In this paper, we describe the data transfer techniques used so far for landing gear andother critical aircraft systems predictive maintenance applications, as well as the available wireless data transfer techniques. Moreover, the architecture of a wireless transfer system forsending aircraft’s landing gear sensory data to a ground station is described, proposing the main guidelines in terms of middleware development and data security and fidelity.