Bongwon Jeong
Papers
1
Total Citations
8
H-Index
1
About
Bongwon Jeong is a researcher whose work lies at the intersection of robotics, machine learning, and uncertainty quantification. His primary research focuses on the stability and robustness of bipedal locomotion, particularly in passive dynamic walkers. Jeong’s most notable contribution is the development of a novel methodology that employs a multi-objective, multi-modal particle swarm optimization (MOMM-PSO) algorithm to explore periodic gaits under uncertainty. This work, published in 2021 and garnering 8 citations, provides a systematic framework for understanding how external disturbances affect walking stability—a critical challenge in legged robotics. By integrating machine learning with classical dynamics, Jeong’s approach enables more resilient gait design, advancing the field toward practical, real-world deployment of biped robots. His research is particularly valuable for students and engineers working on robust control systems, offering a data-driven pathway to optimize locomotion in unpredictable environments. Jeong’s contributions underscore a growing trend in robotics: leveraging computational intelligence to solve complex, nonlinear problems in mechanical systems.
Research Focus
Key Achievements
Top Papers
- 1