Papers
7
Total Citations
194
H-Index
5
About
Chentao Mao is a robotics researcher whose work centers on kinematic calibration, positioning accuracy, and error compensation in industrial and serial robotic systems. With a growing body of highly cited publications, Mao has established himself as a significant contributor to the field of advanced robotic manufacturing, where precision and reliability are paramount. Mao's most influential contributions include pioneering hybrid calibration frameworks that combine model-based geometric identification with artificial neural networks to address both geometric and nongeometric error sources — a challenge that traditional single-method approaches struggle to resolve. His 2019 paper on joint angle division and neural network-based calibration has garnered 56 citations, while his 2020 work on heavy-load robot accuracy improvement has earned 52 citations, reflecting strong community recognition. His development of separable nonlinear least squares algorithms and robust kinematic calibration methods further demonstrates his commitment to mathematical rigor and practical validation. A recurring theme across Mao's research is the compensation of joint deformations caused by heavy payloads and structural flexibility — factors that critically undermine robot performance in real-world manufacturing environments. Collectively accumulating nearly 200 citations, his work offers students and engineers principled, experimentally validated solutions for elevating robotic precision in demanding industrial applications.
Research Focus
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