Papers
3
Total Citations
9
H-Index
2
About
Sheng Li’s research focuses on advancing autonomous navigation and multi-robot coordination, with a particular emphasis on robust control and real-world deployment for wheeled mobile robots. His most cited work, “Robot Operating System-Based SLAM for a Gazebo-Simulated Turtlebot2 in 2d Indoor Environment with Cartographer Algorithm” (2021, 6 citations), provides a practical framework for integrating ROS-based SLAM with the Cartographer algorithm, offering a reproducible benchmark for indoor mapping and localization. Li also made foundational contributions to multi-robot systems with his 2011 paper “A generalized share potential fields approach to multi-robot coordination,” which introduced an efficient, collision-free coordination method for multiple robots in unknown dynamic environments using shared potential fields. More recently, his 2021 work on robust predictive trajectory tracking control addresses the degradation of tracking performance caused by sampling time errors, proposing a multi-model augmented kinematics approach to enhance stability. Collectively, Li’s work bridges simulation and real-world application, providing accessible tools and theoretical insights that support students and researchers in mobile robotics, SLAM, and decentralized control.
Research Focus
Key Achievements
Top Papers
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