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About

Haiyang Tang is a leading researcher in industrial robotics, specializing in precision positioning and error compensation for manufacturing applications. His work focuses on enhancing the absolute position accuracy of robots used in high-stakes environments like aviation assembly, where even minor errors can compromise structural integrity. Tang’s major contributions include pioneering methods that combine error similarity principles with machine learning techniques, such as radial basis function neural networks, to correct positional inaccuracies in real time. His most cited paper, “A positional error compensation method for industrial robots combining error similarity and radial basis function neural network” (2019, 74 citations), introduces a novel framework that leverages spatial error patterns to improve robot accuracy without costly hardware upgrades. Tang also developed an extended Kalman filter-based calibration method for normal sensors in robotic drilling systems (2018, 20 citations), ensuring perpendicularity in critical aerospace tasks. With over 130 total citations, his work is widely referenced by researchers and engineers seeking practical, data-driven solutions for industrial automation. Tang’s research directly addresses the gap between theoretical robot models and real-world performance, making him a key figure in advancing reliable, high-precision robotics for manufacturing.