Yao-Liang Chung
Papers
1
Total Citations
4
H-Index
1
About
Yao-Liang Chung is a researcher specializing in intelligent robotics, control systems, and computational intelligence. His work focuses on integrating fuzzy neural networks and Kalman filters to enhance robotic autonomy and stability, particularly in legged locomotion. In his notable 2018 study, Chung developed an effective hexapod robot control design that enables obstacle avoidance and wall-following behavior. By employing a fuzzy neural network for adaptive decision-making and a Kalman filter for precise posture estimation, his system ensures high stability during movement, dynamically adjusting the robot's orientation relative to walls. This contribution addresses critical challenges in multi-legged robot navigation, balancing real-time sensor fusion with robust control. While his citation count remains modest, Chung's research demonstrates practical innovation in merging soft computing with state estimation for mobile robotics. His work holds potential applications in search-and-rescue, exploration, and industrial automation, where adaptive, stable locomotion is essential. Chung's approach exemplifies how hybrid intelligent systems can improve robot performance in unstructured environments, marking him as a thoughtful contributor to the field of autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1