Contact Dr Ndidiamaka Adiuku
- Email: N.P.Adiuku@cranfield.ac.uk
- ORCID
Background
Dr. Amaka Adiuku is a Robotics and AI Research Fellow whose work sits at the intersection of intelligent automation, computer vision, deep learning, and autonomous systems. Her current research focuses on the development of an AI-powered robotic vision system using an enhanced X-ray image analysis approach for aerospace material inspection, leveraging sophisticated 2D-to-3D visualization techniques developed to detect and quantify structural defects with unparalleled accuracy.
She earned her PhD in Robotics and Machine Learning Systems from Cranfield University, where her thesis introduced a novel deep learning-based real-time obstacle detection, avoidance, and navigation system in dynamic, unstructured environments leveraging the ROS framework, improved sensor fusion, and deep learning models. Prior to her doctoral studies, she completed an MSc in Software Engineering at Kingston University.
Before transitioning to academia, Dr. Adiuku spent several years as an IT manager, leading the design, implementation, and maintenance of large-scale enterprise solutions. In that role, she championed the integration of automation technologies across diverse platforms, significantly streamlining workflows and improving operational efficiency. Today, she continues to build on this experience by fostering close collaborations between research laboratories and industry partners, driving the translation of cutting-edge robotic technologies into real-world applications.
Current activities
With a strong background in computer, machine learning, and industrial automation, she is driving advancements in AI-powered robotics, X-ray image analysis, and visualization for aerospace inspection.
Publications
Articles In Journals
- Adiuku N, Avdelidis NP, Tang G & Plastropoulos A. (2024). Advancements in learning-based navigation systems for robotic applications in MRO hangar: review. Sensors, 24(5)
- Adiuku N, Avdelidis NP, Tang G, Plastropoulos A & Diallo Y. (2024). Mobile robot obstacle detection and avoidance with NAV-YOLO. International Journal of Mechanical Engineering and Robotics Research, 13(2)
- Adiuku N, Avdelidis NP, Tang G & Plastropoulos A. (2024). Improved hybrid model for obstacle detection and avoidance in robot operating system framework (rapidly exploring random tree and dynamic windows approach). Sensors, 24(7)