Papers
21
Total Citations
246
H-Index
8
About
Changbin Yu is a robotics and autonomous systems researcher whose work spans multi-agent coordination, robotic manipulation, navigation, and sensor networks. His research career reflects a sustained commitment to solving fundamental challenges in how robots perceive, plan, and act in complex environments. Yu's early contributions focused on the theoretical foundations of autonomous systems. His 2006 work on rigid formation control and his 2009 multi-agent cooperative mapping research established him as a thoughtful contributor to swarm robotics and distributed systems. His 2012 paper on target localization and circumnavigation by non-holonomic robots — now his most-cited work with 55 citations — elegantly addressed a surveillance challenge where an agent must orbit a target using only bearing angle information, a practically significant constraint in real-world deployments. As deep learning transformed robotics, Yu's research evolved accordingly. His highly cited 2022 work on hierarchical RGB-D fusion for robotic grasping (43 citations) and earlier neural network-based grasping methods demonstrate his fluency bridging classical robotics with modern machine learning. His 2020 multimodal indoor navigation framework further highlights his interest in human-robot interaction through voice and vision. Across more than fifteen years of research, Yu has built an impressive body of work that bridges rigorous mathematical foundations with practical robotic applications, making him a versatile and influential figure in the autonomous systems research community.
Research Focus
Key Achievements
Top Papers
- 1Target localization and circumnavigation by a non-holonomic robot55 citations · 2012
- 2Deep Robotic Grasping Prediction with Hierarchical RGB-D Fusion43 citations · 2022
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- 6Cooperative multi-agent mapping and exploration in Webots®15 citations · 2009
- 7Information Architecture and Control Design for Rigid Formations14 citations · 2006
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- 10UG-Net for Robotic Grasping using Only Depth Image7 citations · 2019