Ying-Han Chen
Papers
1
Total Citations
69
H-Index
1
About
Ying-Han Chen is a leading researcher in intelligent robotics and evolutionary fuzzy control systems. His most influential work focuses on developing data-driven control strategies for autonomous robots, particularly hexapod locomotion. In his highly cited 2014 paper, Chen pioneered the use of an adaptive group-based differential evolution (AGDE) algorithm to train a fuzzy controller for wall-following hexapod robots—a breakthrough that eliminated the need for complex mathematical modeling and reduced time-consuming manual tuning. This approach, which has garnered 69 citations, demonstrates how evolutionary computation can create robust, model-free controllers capable of handling real-world uncertainties. Chen’s contributions lie at the intersection of fuzzy logic, evolutionary optimization, and robotics, offering practical solutions for autonomous navigation in unstructured environments. His work has significant implications for search-and-rescue operations, exploration, and industrial automation where traditional control methods fall short. By combining computational intelligence with mechanical design, Chen has advanced the field of bio-inspired robotics, enabling more adaptive and efficient machines. His research continues to inspire new generations of roboticists and control engineers seeking to push the boundaries of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1