Pengcheng Kan
Papers
2
Total Citations
17
H-Index
2
About
Pengcheng Kan is a rising researcher in the field of human-robot collaborative assembly, with a focused interest in intelligent manufacturing and human-robot interaction. His work centers on developing computer vision and workflow modeling techniques to enhance the safety, efficiency, and intuitiveness of collaborative assembly processes. Kan’s most impactful contribution, "A skeleton-based assembly action recognition method with feature fusion for human-robot collaborative assembly" (2024), has already garnered 15 citations, demonstrating its early influence in the community. This work introduces a novel approach that fuses skeletal features to accurately recognize human assembly actions, enabling robots to better anticipate and respond to human motions. His subsequent paper, "An assembly sequence monitoring method based on workflow modeling for human–robot collaborative assembly" (2024), further advances the field by providing a framework for real-time monitoring and verification of assembly sequences. Through these contributions, Kan is helping to bridge the gap between human dexterity and robotic precision, paving the way for more adaptive and cooperative manufacturing systems. His research holds significant promise for the future of smart factories and human-centric automation.
Research Focus
Key Achievements
Top Papers
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