Xiangwei Mou
Papers
2
Total Citations
9
H-Index
2
About
Xiangwei Mou is a researcher whose work bridges the critical gap between advanced robotics and practical vocational education. His primary research areas encompass industrial robotics curriculum design, agricultural robotics, and 3D computer vision. Mou’s most notable contribution is his innovative application of the Quality Function Deployment (QFD) approach to vocational course design for industrial robotics in China, a methodology that directly translates industry needs into educational outcomes. This work, published in 2022, has already garnered 6 citations, signaling its growing influence on technical education reform. In a parallel line of inquiry, Mou has advanced the field of agricultural automation through his 2021 study on 3D reconstruction for Camellia Oleifera fruit recognition using Kinect cameras. This research, which has earned 3 citations, tackles the complex challenge of enabling outdoor fruit-picking robots to accurately identify and locate mature fruit within their field of view. By integrating computer vision with robotic manipulation, Mou is laying the groundwork for more intelligent and autonomous agricultural systems. His dual focus on both the pedagogical and technical dimensions of robotics makes his work uniquely impactful for students and researchers interested in the future of automation and skill development.
Research Focus
Key Achievements
Top Papers
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