Donghe Yang
Papers
5
Total Citations
148
H-Index
5
About
Donghe Yang is a leading researcher in intelligent robotics, specializing in motion planning, control systems, and visual servoing. His work bridges classical robotics with advanced machine learning and optimization algorithms. Yang’s most influential contribution is his 2017 paper on tangent navigated robot path planning using a particle swarm optimized artificial potential field, which has garnered 93 citations and provides a robust solution for autonomous navigation in complex environments. He has also advanced manipulator control through sliding mode control integrated with a hybrid grey-wolf-optimized extreme learning machine (2019, 34 citations), significantly improving precision and robustness. In visual servoing, Yang addressed critical challenges in uncalibrated environments by developing a Kalman filter-optimized extreme learning machine with fuzzy logic (2022), mitigating perturbation noises and slow convergence. His earlier work on ultrasound-based obstacle avoidance for mobile robots (2010) laid foundational insights, while his research on multiple instance learning tracking using Fisher linear discriminant (2018) contributed to semi-supervised object tracking. With a consistent focus on enhancing robot autonomy and reliability, Yang’s work is widely cited and continues to influence both theoretical and applied robotics research.
Research Focus
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