Papers
5
Total Citations
45
H-Index
5
About
Haoting Liu’s research lies at the intersection of laser material processing, robotic vision, and surface quality assessment, with a focus on advancing automated manufacturing and inspection systems. His major contributions include developing novel image-based methods for estimating surface roughness after laser cleaning, notably through enhanced GLCM features and an IPSO-SVR model, which achieved 12 citations for its practical accuracy. He also pioneered a two-stage automatic parameter tuning algorithm for laser cleaning that integrates surface roughness estimation, cited 10 times for its efficiency in optimizing industrial processes. In robotics, Liu designed a Space Photographic Robotic Arm (SPRA) with binocular vision servo control for in-orbit imaging tasks, and a defect segmentation technique for fiber splicing using Gaussian Mixture Models and graph cut—both demonstrating his ability to solve real-world quality control challenges. His work on imaging quality-based state switch algorithms for outdoor moving robots further underscores his expertise in robust computer vision. With over 45 total citations across these key papers, Liu’s research has tangible impact on precision manufacturing and space robotics, making him a notable figure in applied computer vision and automation.
Research Focus
Key Achievements
Top Papers
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