Papers
15
Total Citations
351
H-Index
8
About
Dr. Mao Shan is a leading researcher in multi-robot systems, specializing in formation control, autonomous navigation, and cooperative localization for nonholonomic mobile robots. His most impactful contribution is pioneering the concept of *flexible* leader–follower formations, a significant departure from traditional rigid formations. His seminal 2019 paper on this topic (103 citations) defines formations using curvilinear coordinates, enabling robots to adapt to unknown, obstacle-filled environments. This work, along with his practical tracking control scheme (59 citations), addresses real-world challenges like sensor noise and obstacle avoidance, moving beyond purely theoretical approaches. Dr. Shan has also made notable advances in autonomous target docking (51 citations) and deep reinforcement learning for visual navigation (27 citations). To solve practical localization issues, he developed a cooperative system using ultra-wideband sensors with GPU acceleration (26 citations) and a Bayesian filtering method for error mitigation. His research consistently bridges the gap between theoretical control and robust, real-world deployment, making him a key figure in advancing the autonomy and practicality of multi-robot teams.
Research Focus
Key Achievements
Top Papers
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- 4Kalman filtering under unknown inputs and norm constraints34 citations · 2021
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