About

Kyunghwan Kim is a robotics researcher whose work spans an impressive breadth of domains, from construction automation and surgical robotics to wearable systems and machine learning-driven medical devices. His early contributions in the late 1990s and early 2000s focused on wearable robotic arms and exoskeletal systems, with multiple publications exploring pneumatic actuation, force reflection, and human-robot interaction — work that laid important groundwork for modern rehabilitation and teleoperation technologies. His most cited work, a laser-based lifting-path tracking system for robotic tower cranes (2009, 105 citations), demonstrated his ability to bridge theoretical robotics with real-world industrial applications. Kim has also made significant inroads into minimally invasive surgery, developing tele-operated master-slave systems for neurosurgery and, more recently, applying deep reinforcement learning to automate guidewire navigation in coronary artery procedures (2021, 53 citations) — a clinically meaningful advance given the steep learning curve of percutaneous interventions. His work on Delta-type parallel robot dynamics and cooperative multi-robot manipulation further reflects his versatility across mechanical modeling and control. Collectively, Kim's portfolio reveals a researcher consistently pushing robotic systems closer to human-scale precision and real-world clinical and industrial utility.

Research Focus

Key Achievements

8
H-Index
15
Papers
304
Total Citations
20
Avg Citations/Paper
🏆 Most Cited Paper
A laser-technology-based lifting-path tracking system for a robotic tower crane
105 citations · 2009
📈 Most Prolific Year: 2002 (4 Papers)
🤝 Key Collaborators: 50
🏛 Institutions: Konkuk University, Medipost (South Korea), Korea Institute of Science and Technology, The University of Tokyo, Mitchell Institute, Korea Institute of Robot and Convergence

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago