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About

Geonuk Lee is a robotics researcher whose work focuses on autonomous systems for logistics and industrial applications, particularly in unstructured and confined environments. His key research areas include autonomous unloading systems, robotic navigation, and computer vision for object handling. Lee’s most impactful contribution is his 2024 paper on unloading sequence planning for autonomous robotic container-unloading systems, which uses an A-star search algorithm to prevent package damage and collisions in cluttered logistics environments—a critical challenge for warehouse automation. This work has already garnered 22 citations, highlighting its relevance to the field. He has also advanced in-pipe robot navigation with a novel rapidly exploring random tree (RRT) approach, published in 2017, and developed a vision system for box segmentation and size estimation in 2022, enabling reliable robotic handling. Lee’s research addresses practical bottlenecks in logistics robotics, from navigation to manipulation, and his citation record reflects growing interest in his solutions. His work stands out for its focus on real-world deployment, making him a notable contributor to autonomous systems in logistics.