Papers
4
Total Citations
47
H-Index
3
About
Herman Fesenko is a leading researcher at the intersection of robotics, artificial intelligence, and humanitarian demining. His work focuses on developing autonomous systems for the detection and identification of explosive ordnance (EO), a critical area for post-conflict recovery and civilian safety. Fesenko’s major contributions include pioneering the concept of robotic-biological systems (RBS-D&I) that integrate biological sensors with robotic platforms for enhanced EO detection, as detailed in his most-cited 2023 paper (33 citations). He has also advanced the practical deployment of these systems through algorithms for UAV fleet management with automatic battery replacement, enabling persistent accident monitoring over critical infrastructure. His recent 2024 work on deep learning models—specifically comparing YOLOv8 and RT-DETR—addresses the crucial trade-off between detection accuracy and real-time processing speed for autonomous demining robots. With a growing citation record and a clear trajectory from conceptual frameworks to applied AI solutions, Fesenko is establishing himself as a key innovator in making demining operations safer, faster, and more effective through intelligent robotics.
Research Focus
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