Papers
4
Total Citations
60
H-Index
3
About
Dong-oh Kang is a leading figure in robotics, whose work spans adaptive locomotion, assistive navigation, and distributed multi-robot intelligence. His foundational research on quadruped walking robots introduced a groundbreaking adaptive gait control algorithm that enables stable locomotion under external forces—a critical contribution to legged robotics that has garnered 36 citations. Kang’s commitment to human-centered robotics is evident in his pioneering work on a guide mobile robot for the visually impaired, where he developed multiobjective navigation strategies based on obstacle intention inference, earning 14 citations. He further advanced mobile robot autonomy with multiple reward reinforcement learning for home network environments (8 citations). Most recently, Kang has pushed the frontier of multi-robot systems with his 2024 work on distributed deep learning for real-world implicit mapping, where 2D LiDAR data is shared wirelessly among robots to create integrated environmental maps in real time. This innovative approach promises to revolutionize collaborative robotics in dynamic, unstructured settings. With a career spanning over two decades, Kang’s research consistently bridges theoretical rigor and practical application, making him a vital contributor to the fields of adaptive robotics, assistive technology, and distributed intelligence.
Research Focus
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Top Papers
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