Botao Yang
Papers
1
Total Citations
10
H-Index
1
About
Botao Yang is a researcher advancing the field of human-robot collaboration through intelligent perception and safety systems. His work focuses on integrating scene semantic information into dynamic speed and separation monitoring, a critical area for ensuring safe and efficient human-robot interaction in industrial and service environments. Yang’s most-cited paper, "Dynamic Speed and Separation Monitoring Based on Scene Semantic Information" (2022), has garnered 10 citations, reflecting its growing influence in the robotics community. This research introduces a novel approach that leverages semantic understanding of the environment—such as object recognition and spatial context—to dynamically adjust robot speed and separation distances, enhancing both safety and productivity. By moving beyond traditional static safety zones, Yang’s contributions enable robots to operate more fluidly alongside humans, reducing downtime and improving workflow. His work is particularly notable for bridging computer vision and robotics, offering practical solutions for real-time, context-aware safety. As a rising voice in this domain, Botao Yang’s research is shaping the next generation of adaptive, intelligent robotic systems, with implications for manufacturing, logistics, and beyond.
Research Focus
Key Achievements
Top Papers
- 1Dynamic Speed and Separation Monitoring Based on Scene Semantic Information10 citations · 2022