Ndidiamaka Adiuku
Papers
4
Total Citations
38
H-Index
4
About
Ndidiamaka Adiuku is an emerging researcher specializing in mobile robotics, autonomous navigation, and intelligent systems, with a particular focus on real-world industrial applications. Their work sits at the intersection of machine learning, computer vision, and robotic systems, addressing some of the most pressing challenges in dynamic and unpredictable environments. Adiuku's most significant contributions center on advancing obstacle detection and avoidance for mobile robots, particularly within Maintenance, Repair, and Overhaul (MRO) hangar operations for aircraft visual inspection. Their 2024 paper introducing a hybrid model combining Rapidly Exploring Random Tree (RRT) and Dynamic Windows Approach within the Robot Operating System framework has garnered 18 citations, establishing it as a notable contribution to autonomous navigation research. Complementing this, their development of NAV-YOLO — a specialized navigation and detection system for complex hangar environments — demonstrates a strong drive toward practical, deployable robotics solutions. With a comprehensive review of learning-based navigation systems and pioneering work in CNN-fusion architectures combining visual and thermographic imaging, Adiuku has collectively accumulated nearly 40 citations across publications from 2023–2024 alone. This rapid citation growth signals a researcher whose work is gaining meaningful traction in both academic and industrial robotics communities, making their contributions essential reading for those exploring autonomous inspection and intelligent navigation systems.
Research Focus
Key Achievements
Top Papers
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- 2Mobile Robot Obstacle Detection and Avoidance with NAV-YOLO9 citations · 2024
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