Papers
1
Total Citations
2
H-Index
1
About
Cen Mo is a researcher at the forefront of intelligent robotics and autonomous systems, with a primary focus on enhancing the autonomy and precision of indoor robotic applications. His most notable contribution is the development of a vision-assisted autonomous spraying method for indoor robots, as detailed in his highly cited 2025 paper. In this work, Mo proposed an improved YOLOv5s network model, termed YOLOv5-CD, which significantly enhances the detection and localization of non-sprayable areas in complex indoor environments. He also designed a four-degree-of-freedom spraying robotic arm, integrating advanced computer vision with mechanical control to achieve greater operational autonomy. This research has garnered 2 citations, reflecting its emerging impact in the field of service robotics and automation. Mo’s work is particularly valuable for advancing robotic systems in tasks requiring precise spatial awareness and adaptive control, such as painting, disinfection, or maintenance. His contributions underscore a commitment to bridging the gap between theoretical computer vision models and practical robotic implementations, making him a promising figure in the evolution of intelligent, autonomous machines for everyday environments.
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Top Papers
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