Papers
7
Total Citations
108
H-Index
5
About
Deyong Shang is a leading researcher in robotics, with a primary focus on the kinematic modeling, error analysis, and motion control of parallel robots, particularly the Delta robot. His major contributions lie in systematically addressing motion accuracy degradation caused by mechanism errors—such as parallelism errors in parallelogram-driven arms—and developing compensation strategies to enhance performance. Shang’s work has direct industrial applications, notably in intelligent coal-gangue separation, where he designed a Delta-type parallel robot that uses image recognition to automatically sort coal and gangue on conveyor belts, a key innovation for smart mining. His most-cited paper, “Kinematic modeling and error analysis of Delta robot considering parallelism error” (2019, 27 citations), along with subsequent studies on comprehensive calibration and motion reliability (each garnering 19–22 citations), have collectively shaped the field of high-speed sorting robotics. He has also explored rigid-flexible coupling dynamics in welding robots and adaptive control for variable-load sorting. With over 100 total citations, Shang’s research bridges theoretical error analysis and practical robotic sorting, making him a notable figure in precision parallel robotics.
Research Focus
Key Achievements
Top Papers
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- 5Study on the Rigid-Flexible Coupling Dynamics of Welding Robot14 citations · 2018
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