Soohwan Song

Korea Advanced Institute of Science and Technology, Electronics and Telecommunications Research Institute

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About

Soohwan Song is a robotics researcher whose work spans autonomous exploration, 3D environmental modeling, multi-agent systems, and agricultural robotics. His most influential contribution, "Surface-Based Exploration for Autonomous 3D Modeling" (2018), has garnered 67 citations and represents a significant advancement in mobile robot path planning. Rather than relying on conventional volumetric map analysis, Song pioneered a surface-based approach that enables robots to construct highly accurate 3D models of previously unknown environments — a methodology that has resonated strongly within the autonomous robotics community. Building on his expertise in intelligent navigation, Song extended his research to multi-agent pathfinding, addressing collision-free coordination of multiple robots within topologically constrained corridor environments. His 2022 work introduces corridor occupancy-based strategies that improve upon standard MAPF algorithms in real-world deployment scenarios. More recently, Song has bridged fundamental robotics research with practical agricultural applications, developing remote driving systems for robotic sprayers operating in challenging orchard environments — work that speaks directly to pressing global concerns around agricultural labor shortages and automation. Collectively, his research demonstrates a rare ability to advance both theoretical foundations and real-world robotic applications across diverse and demanding domains.