Max Robotics — Global Service Robot Directory + US Certification

About

DongKi Noh is a leading researcher in autonomous mobile robotics, with a focus on cleaning robots, surveillance systems, and multi-agent coordination. His work addresses critical challenges in real-world robot deployment, including obstacle detection, path planning, and simultaneous localization and mapping (SLAM). Noh’s most cited paper, “A robust obstacle detection method for robotic vacuum cleaners” (2014, 43 citations), introduced a novel approach using structured light and sensor fusion to help robots navigate complex environments like furniture with thin legs—a practical problem that has influenced commercial vacuum cleaner design. He further advanced cleaning robot autonomy with “Adaptive Coverage Path Planning Policy for a Cleaning Robot with Deep Reinforcement Learning” (2022, 16 citations), applying actor-critic models to optimize cleaning efficiency. Noh also contributed to large-scale surveillance with the X-MAS dataset (2023, 10 citations), a multi-modal sensor dataset for outdoor security, and the MASS system (2022, 8 citations) for scheduling multiple surveillance robots. His recent work, “CLOi-Mapper” (2024), delivers a lightweight, robust SLAM solution for embedded systems in commercial service robots. With a career spanning from 2005 to the present, Noh’s research consistently bridges theoretical algorithms and practical, consumer-grade applications, making him a key figure in the evolution of intelligent, autonomous service robots.