Papers
29
Total Citations
286
H-Index
9
About
Dong-Won Kim is a leading researcher in robotics, specializing in bipedal walking, humanoid control, and intelligent navigation systems. His work centers on the zero-moment point (ZMP) trajectory modeling for biped robots, where he pioneered the use of adaptive neuro-fuzzy systems and fuzzy logic to stabilize walking dynamics—a critical challenge in humanoid robotics. His most cited paper (2005, 46 citations) introduced a ZMP trajectory model using an adaptive neuro-fuzzy system, laying the groundwork for smoother, more natural bipedal locomotion. Kim further advanced the field by integrating support vector regression for stable trajectory generation (2009, 16 citations) and neural network-inspired control (2011, 20 citations). In mobile robotics, he developed an advanced fuzzy potential field method for obstacle avoidance (2016, 29 citations) and autonomous multi-robot systems using fuzzy logic (2013, 19 citations). His contributions extend to service robotics, including door-opening mechanisms (2004, 19 citations) and humanoid robots for home environments. Kim’s innovative use of interval type-2 fuzzy sliding mode control (2017, 10 citations) and ego-motion-compensated object recognition (2013, 10 citations) demonstrates his commitment to robust, real-world robot performance. With over 200 citations across his top works, Kim’s research continues to shape the future of intelligent, autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance29 citations · 2016
- 3ZMP based neural network inspired humanoid robot control20 citations · 2011
- 4Fuzzy Modeling of Zero Moment Point Trajectory for a Biped Walking Robot20 citations · 2004
- 5
- 6Mobile Robot for Door Opening in a House19 citations · 2004
- 7
- 8Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator10 citations · 2017
- 9
- 10Service-provider intelligent humanoid robot using TCP/IP and CORBA9 citations · 2016