Bahrudin

National Taiwan University of Science and Technology

Papers

Total Citations

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About

Bahrudin is a researcher focused on advancing autonomous systems and robotics, with key contributions in deep learning for unmanned aerial vehicles (UAVs) and sustainable energy management for disaster response robots. His most-cited work, "Landing Area Recognition using Deep Learning for Unmanned Aerial Vehicles" (2020, 20 citations), addresses a critical barrier to civilian UAV operations: the lack of automated landing site detection. By proposing a deep learning-based localization system, Bahrudin’s research enables safer, more reliable UAV flight over populated areas, supporting logistical transport and broader integration into civilian airspace. This work stands out for its practical impact on real-world UAV autonomy. Additionally, his study "Energy Management System for Sustainable Operation of Robot in Disaster Response" (2020, 2 citations) tackles the challenge of extending mission endurance for search and rescue robots through photovoltaic energy harvesting and intelligent management, despite solar fluctuations. While less cited, this work highlights his commitment to resilient, energy-autonomous systems for critical applications. Bahrudin’s research bridges computer vision and robotics, offering tangible solutions for autonomous navigation and sustainable operation, making him a notable contributor to the fields of UAV technology and disaster robotics.