Byungsoo Kim

University of Michigan–Ann Arbor, Kyung Hee University

Papers

Total Citations

206

H-Index

About

Byungsoo Kim’s research lies at the intersection of computer vision, robotics, and autonomous systems, with a focus on enabling machines to perceive and navigate the physical world. His most cited work, “Comparing image classification methods: K-nearest-neighbor and support-vector-machines” (2012, 135 citations), addresses a foundational challenge in computer vision: how computers can reliably recognize objects in images. This paper provides a systematic comparison of two classic algorithms, offering practical insights for building classification systems—a critical step for robots and autonomous agents that must interpret their surroundings. In robotics, Kim has advanced multi-robot coordination with “Time-efficient and complete coverage path planning based on flow networks for multi-robots” (2013, 38 citations), introducing a novel approach to ensure efficient, thorough area coverage—essential for applications like search-and-rescue or environmental monitoring. Earlier, his work on “Variable geometry single-tracked mechanism for a rescue robot” (2005, 33 citations) tackled the mechanical design of robots for off-road mobility, directly contributing to the development of more capable rescue vehicles. With over 200 total citations, Kim’s contributions bridge algorithmic and mechanical innovation, making him a notable figure in applied robotics and machine perception.