Ziyuan Guo
Papers
2
Total Citations
55
H-Index
2
About
Dr. Ziyuan Guo is a researcher at the intersection of robotics and computer vision, whose work advances intelligent automation in construction and object detection. His primary research areas include robotic perception and manipulation for construction tasks, as well as deep learning-based object detection for autonomous systems. Dr. Guo’s most notable contribution is his pioneering work on robotic rebar binding, where he developed a system that integrates active perception and planning to enable robots to autonomously handle complex, unstructured construction environments—a critical step toward automating labor-intensive building processes. This work, published in 2021, has already garnered 48 citations, reflecting its growing influence in construction robotics. Additionally, his 2020 study on multi-scale object detection using a feature fusion recalibration network addresses the challenge of scale invariance in deep learning algorithms for robot platforms, achieving a balance between detection efficiency and accuracy across object sizes. With a total of 55 citations across his top papers, Dr. Guo’s research is shaping the future of intelligent robotics in real-world applications, from construction sites to autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1Robotic binding of rebar based on active perception and planning48 citations · 2021
- 2Multi-Scale Object Detection Using Feature Fusion Recalibration Network7 citations · 2020