Papers
4
Total Citations
28
H-Index
3
About
Shu Li is an emerging researcher whose work sits at the intersection of mobile robotics, autonomous control systems, and adaptive learning algorithms. With a focused body of research on wheeled and nonholonomic mobile robots, Li has made notable contributions to the challenges of real-world robot navigation and control in complex, unstructured environments. Li's most-cited work, "Efficient Online Planning and Robust Optimal Control for Nonholonomic Mobile Robot in Unstructured Environments" (2024, 14 citations), addresses one of robotics' most persistent challenges: enabling reliable autonomous operation when terrain and surroundings are unpredictable. Building on this foundation, Li has pioneered adaptive reinforcement learning frameworks for vehicle tracking control, including novel event-triggered neural network approaches applied to sophisticated four-wheel independent steering and driving platforms (8 citations). More recent contributions extend this expertise to soft deformable terrain environments, where wheel-ground interaction dynamics complicate motion control, and to self-reconfigurable wheeled mobile robots, where Li investigates traction enhancement and energy optimization through intelligent mode-switching strategies. Collectively, Li's rapidly growing citation record reflects meaningful advances in making autonomous mobile robots smarter, more energy-efficient, and more resilient across diverse operational conditions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4