Papers
38
Total Citations
628
H-Index
14
About
Yong Guan is a prolific robotics and systems researcher whose work spans real-time robotic architectures, intelligent motion planning, formal verification, and surgical robotics. His foundational contributions to the Robot Operating System (ROS) ecosystem — particularly his highly cited RT-ROS framework (2015, 91 citations) and its predecessor RGMP-ROS — addressed critical real-time performance limitations on multi-core processors, enabling more reliable and time-sensitive robotic applications. Building on this infrastructure work, Guan has made significant strides in robotic intelligence, notably advancing deep reinforcement learning for trajectory planning through novel dense reward functions that substantially improve learning efficiency in obstacle-rich environments (2019, 75 citations). His formal model-based design methodology (2018, 47 citations) and real-time verification frameworks further demonstrate his commitment to software correctness and safety in increasingly complex robotic systems. Guan has also contributed to surgical robotics, designing a magnetically actuated insertable camera system for minimally invasive surgery and developing unsupervised surgical trajectory segmentation techniques. Spanning swarm robotics, kinematics, and DDS verification in ROS2, his diverse yet cohesive body of work positions him as a versatile and impactful figure in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1RT-ROS: A real-time ROS architecture on multi-core processors91 citations · 2015
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- 3A Formal Model-Based Design Method for Robotic Systems47 citations · 2018
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- 5From Offline Towards Real-Time Verification for Robot Systems38 citations · 2018
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- 8Formal Analysis and Verification of DDS in ROS222 citations · 2018
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