Kuei‐Yuan Chan
Papers
6
Total Citations
42
H-Index
4
About
Kuei-Yuan Chan is a robotics researcher whose work centers on precision, uncertainty quantification, and autonomous navigation in robotic systems. His most significant contributions lie in improving the operational accuracy of robot manipulators by systematically identifying and modeling sources of uncertainty—both geometric and nongeometric—that compromise real-world performance. His 2016 optimization technique for identifying robot manipulator parameters under uncertainty stands as his most influential work, accumulating 19 citations, and laid the groundwork for subsequent investigations into joint clearance identification and the dynamic performance of serial and parallel robot systems. Chan's research distinguishes itself by moving beyond conventional calibration approaches to interrogate the root causes of manipulation error, including gear transmission imperfections and joint degradation. More recently, his focus has expanded toward autonomous mobile robotics, with contributions addressing dynamic obstacle avoidance using Bayesian optimization and object tracking, as well as novel path-planning strategies for constrained environments. Collectively, his body of work bridges theoretical uncertainty modeling and practical engineering solutions, making it particularly valuable for researchers and engineers working on industrial automation, robot calibration, and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Identifying joint clearance via robot manipulation7 citations · 2017
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