Qingshan Liu
Papers
12
Total Citations
168
H-Index
7
About
Qingshan Liu is a prominent researcher specializing in distributed optimization, multi-robot systems, and intelligent control, with a particular focus on bridging advanced mathematical frameworks with practical robotics applications. His work has made significant contributions to multi-robot formation control, leveraging distributed optimization algorithms to coordinate robot behavior through virtual reference centers and projection-based methods, accumulating over 44 citations on his most influential work alone. Liu has pioneered the application of deep reinforcement learning and neural network estimators to multi-robot coordination, developing hierarchical frameworks that address real-world challenges such as disturbance rejection, fault detection and injection attacks, and nonholonomic motion constraints. His research extends into cooperative transportation, localization, and trajectory tracking for mobile robots with redundant manipulators, demonstrating a broad systems-level perspective on autonomous robotics. Notably, Liu has advanced the emerging field of predefined-time and aggregative optimization for nonlinear multi-agent systems, producing several high-impact publications in 2024–2025 that reflect the cutting-edge direction of his research program. With a growing body of work totaling over 165 citations, Liu represents an increasingly influential voice in intelligent multi-robot systems research.
Research Focus
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