Papers
9
Total Citations
348
H-Index
5
About
SeungBeum Suh is a multidisciplinary robotics researcher whose work spans autonomous navigation, artificial intelligence, and human-robot interaction. With expertise bridging mechanical engineering and emerging technology adoption, Suh has made notable contributions to the fields of mobile robotics, sensor fusion, and AI-driven systems in both industrial and service contexts. Suh's most impactful work examines how consumers accept AI-powered robots in hospitality and tourism settings, with his interactive Technology Acceptance Model (iTAM) framework garnering 192 citations and establishing him as a key voice in service robotics research. His earlier technical contributions focused on autonomous urban navigation, developing sophisticated LiDAR-based road boundary detection using interacting multiple Kalman filters — a paper cited over 100 times — alongside robust lane recognition and sensor fusion algorithms that advanced reliable unmanned vehicle navigation in GPS-degraded environments. Suh has also applied robotics expertise to critical real-world challenges, including designing mine detection robots tailored to Korean minefields and developing AI-powered autonomous disinfection robots in response to the COVID-19 pandemic. Together, his body of work, accumulating over 340 citations, reflects a career dedicated to translating advanced robotics and AI into practical, societally meaningful applications across safety, healthcare, and service industries.
Research Focus
Key Achievements
Top Papers
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- 4Design of mine detection robot for Korean mine field8 citations · 2010
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- 6Sensor fusion-based line detection for unmanned navigation5 citations · 2010
- 7Mine Detecting Robot System4 citations · 2013
- 8Autonomous urban navigation and its application to patrol3 citations · 2010
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