Minrui Yan
Papers
6
Total Citations
47
H-Index
3
About
Minrui Yan is an emerging researcher specializing in industrial robot calibration, precision positioning, and intelligent optimization techniques. His work sits at the intersection of robotics, machine learning, and metaheuristic algorithms, with a particular focus on enhancing the accuracy and reliability of industrial robotic systems. Yan's most influential contribution, "Precision Denavit–Hartenberg Parameter Calibration for Industrial Robots Using a Laser Tracker System and Intelligent Optimization Approaches" (2023, 27 citations), addresses a fundamental challenge in industrial automation: achieving accurate end-effector positioning through refined forward kinematics modeling. Complementing this, his neural network separation approach for static friction modeling (12 citations) demonstrates a sophisticated understanding of nonlinear robot dynamics, offering a novel framework for incorporating friction effects into robot models with greater fidelity. Across his portfolio, Yan consistently employs advanced tools—including artificial bee colony algorithms, interval type-2 fuzzy logic systems, and laser tracker feedback—to push the boundaries of robotic precision. His research on XY-linear stages and mechanically modified robots further illustrates his versatility in tackling real-world positioning challenges. With over 40 cumulative citations primarily within a single year of publication, Yan is rapidly establishing himself as a promising voice in intelligent industrial robotics calibration.
Research Focus
Key Achievements
Top Papers
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