Guoqian Zhang
Papers
3
Total Citations
43
H-Index
2
About
Guoqian Zhang is a leading researcher at the intersection of robotics and sports engineering, with a primary focus on intelligent tennis-training systems and redundant robot control. His work addresses critical challenges in robotic precision and adaptability, particularly for applications requiring high-accuracy manipulation and dynamic interaction. Zhang's most impactful contribution is a recurrent neural network (RNN)-based quadratic programming scheme for tennis-training robots, which overcomes the limitations of traditional launching machines by enhancing control accuracy and flexibility—a paper that has garnered 24 citations since 2022. He further advanced the field with a data-driven remote center of cyclic motion (RC²M) control method for redundant robots equipped with rod-shaped end-effectors, achieving 17 citations by 2024. This work tackles the persistent issue of RCM point deviation, crucial for minimally invasive surgical robots and similar applications. Additionally, his 2023 study on ball-spin control and vibration reduction for three-wheel pitching devices demonstrates his commitment to refining robotic performance in real-world sports training. With a growing citation record and a focus on bridging theoretical control methods with practical robotic systems, Zhang is shaping the future of interactive and assistive robotics.
Research Focus
Key Achievements
Top Papers
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