Contact Ndidiamaka Adiuku
- Email: N.P.Adiuku@cranfield.ac.uk
- ORCID
Background
Amaka Adiuku completed her BEng and obtained MSc in Software Engineering from Kingston University London and is now a Researcher at Cranfield University. She has worked as an IT specialist, in many organizations where she oversees development and operations of enterprise solutions, platforms, and technologies that drive automation, integrations and efficiency through cross-platforms.
She is pursuing her PhD research which focuses on Machine learning approach for autonomous vehicle navigation to enhance and automate aircraft visual inspections.
Her key research areas are robotics, reinforcement learning, deep learning, object detection and application to improve system autonomy and automation in industries like aerospace.
Current activities
Amaka's research focuses on developing an improved object detection and obstacle avoidance model using machine learning techniques for efficient Robot navigation in in unstructured environment.
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. Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach). Sensors, 24(7)
Conference Papers
- Adiuku N, Avdelidis NP, Tang G, Plastropoulos A & Perinpanayagam S. (2023). CNN-fusion architecture with visual and thermographic images for object detection
- Plastropoulos A, Adiuku N, Avdelidis NP, Ibarra-Castanedo C, Maldague X, .... (2023). A short review on infrared thermography: history, philosophy, approaches, standards, and application examples