Hengkang Shao

Papers

Total Citations

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About

Hengkang Shao is a robotics researcher whose work centers on advancing the precision and autonomy of robotic systems, with a particular focus on serial robot self-calibration and human-robot interaction. His most influential contribution, "An online method for serial robot self-calibration with CMAC and UKF" (2016, 26 citations), introduces a novel approach that combines cerebellar model articulation controllers (CMAC) with unscented Kalman filters (UKF) to enable real-time, high-accuracy calibration of robotic arms—a critical challenge in industrial automation. This work has been widely recognized for improving robot reliability without costly offline procedures. Shao also developed "A human–robot interface using particle filter, Kalman filter, and over-damping method" (2016, 4 citations), which integrates advanced filtering techniques with damping strategies to create smoother, more intuitive control interfaces, enhancing safety and user experience in collaborative settings. His research bridges theoretical control methods with practical robotics applications, offering scalable solutions for manufacturing, healthcare, and service robotics. Shao’s contributions continue to influence the development of adaptive, human-friendly robotic systems.