Yonghua Bai
Papers
3
Total Citations
144
H-Index
3
About
Yonghua Bai is a leading researcher in robotics, specializing in robot kinematics, positioning, and calibration. His work addresses critical challenges in robotic control accuracy, particularly through innovative optimization algorithms. Bai’s most cited paper (59 citations) introduces a Fruit Fly Optimization Algorithm (FOA) to enhance BP neural networks for solving robot inverse kinematics, significantly improving solution speed and accuracy over traditional methods. He further advanced indoor mobile robot positioning with an adaptive federated Kalman filter (50 citations), overcoming the limitations of single-sensor systems by fusing multiple data sources for robust localization. His recent contribution (35 citations) presents a novel calibration method using an improved Manta Ray Foraging Optimization (MRFO) algorithm to reduce absolute positioning errors in robotic arms, directly impacting industrial precision. With over 144 total citations, Bai’s work is pivotal for autonomous navigation and manufacturing, offering practical solutions that bridge algorithmic theory and real-world robotics. His research is essential reading for engineers and students seeking to optimize robot performance through intelligent, nature-inspired computation.
Research Focus
Key Achievements
Top Papers
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