Papers
15
Total Citations
115
H-Index
7
About
Dayong Yu is a robotics researcher whose work centers on the kinematic calibration and accuracy enhancement of parallel robots, with particular emphasis on their application in spacecraft docking mechanism motion simulators. Over nearly two decades of sustained research, Yu has made significant contributions to solving one of the most persistent challenges in parallel robotics: achieving precise pose accuracy in complex, high-stakes environments. Yu's methodological innovations span multiple approaches to kinematic calibration, including nonlinear least squares estimation, total least squares algorithms, L-infinity parameter estimation, and improved particle swarm optimization — demonstrating both breadth and evolution in his technical toolkit. Complementing this calibration work, he pioneered the use of artificial neural networks, specifically backpropagation architectures, for pose accuracy compensation in parallel robots, an approach that has proven influential enough to appear across multiple publication venues. His error modeling work for space docking semi-physical simulation platforms further underscores his focus on real-world engineering applications with demanding precision requirements. His most cited work, a 2006 paper on CMM-based kinematic calibration (22 citations), remains a foundational reference in the field. Collectively accumulating over 100 citations, Yu's research offers students and engineers a comprehensive body of work bridging theoretical kinematics with practical aerospace simulation challenges.
Research Focus
Key Achievements
Top Papers
- 1Kinematic Calibration of Parallel Robots Using CMM22 citations · 2006
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- 3Parallel robots pose accuracy compensation using artificial neural networks14 citations · 2005
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- 9Kinematic calibration of parallel robots6 citations · 2006
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