Papers
3
Total Citations
29
H-Index
2
About
Jianda Shao is a leading researcher in ultra-precision optical manufacturing, with a primary focus on advancing computer-controlled subaperture polishing and industrial robot-based polishing technologies. His major contributions center on solving the critical problem of midspatial frequency (MSF) error—or surface ripple—which degrades the performance of high-end optical systems. Shao pioneered high-efficiency pseudo-random path planning to suppress ripple in robotic polishing, a breakthrough detailed in his most-cited work (2021, 23 citations). He further developed a plug-and-play positioning error compensation model (2023, 4 citations) to mitigate ripple caused by industrial robot inaccuracies, enhancing the viability of low-cost, high-degree-of-freedom robotic polishers. In a notable achievement, Shao introduced a statistical perception framework that treats fabrication errors as chaotic phenomena, enabling self-adaptive processing decisions for ultra-precision optics (2023, 2 citations). This work moves beyond traditional physical modelling to harness data-driven error correction. His research is instrumental in pushing the limits of optical fabrication, directly impacting industries reliant on high-performance lenses and mirrors. Shao’s innovative, cross-disciplinary approach—blending robotics, control theory, and statistical analysis—positions him as a key figure in next-generation optical manufacturing.
Research Focus
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