Quan Quan
Papers
22
Total Citations
274
H-Index
10
About
Quan Quan is a prominent robotics researcher whose work spans swarm robotics, trajectory planning, and autonomous control systems. Best known for pioneering the concept of **virtual tubes** for robotic swarms, Quan has developed a suite of innovative methods enabling drone and robot collectives to navigate safely and efficiently through cluttered, obstacle-dense environments. His foundational papers on distributed swarm control within curve virtual tubes, optimal virtual tube planning, and the regular virtual tube model have collectively garnered over 130 citations since 2022, reflecting the rapid adoption of his ideas within the robotics community. Quan's contributions extend beyond swarm navigation. His non-potential orthogonal vector field method offers a more efficient paradigm for general robot navigation, while his earlier work on filtered repetitive controllers demonstrates long-standing expertise in nonlinear control theory. His bibliometric analysis of UAV swarms has also served as a valuable resource for researchers mapping the field's growth. More recently, Tube RRT* introduced an efficient homotopic path planning algorithm tailored for large-scale environments, and his design automation methodology for multicopter UAVs addresses practical engineering challenges. Across his career, Quan's research consistently bridges theoretical rigor with real-world swarm robotics applications.
Research Focus
Key Achievements
Top Papers
- 1Distributed control for a robotic swarm to pass through a curve virtual tube40 citations · 2023
- 2Optimal virtual tube planning and control for swarm robotics34 citations · 2023
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- 4Making Robotics Swarm Flow More Smoothly: A Regular Virtual Tube Model24 citations · 2022
- 5A Filtered Repetitive Controller for a Class of Nonlinear Systems22 citations · 2010
- 6Bibliometric analysis of UAV swarms20 citations · 2022
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