Papers
2
Total Citations
10
H-Index
2
About
Jingye Li is a robotics researcher specializing in intelligent motion control and autonomous navigation for mobile robots operating in complex, human-inhabited environments. Their work bridges deep reinforcement learning and non-holonomic constraint theory to solve fundamental challenges in robot mobility. Li's most cited paper (2019, 7 citations) introduces a novel point stabilization kinematic control law that leverages deep reinforcement learning to address the motion control problem of non-holonomic constrained mobile robots—a critical advancement for robots that cannot move in all directions. By constructing a kinematic model that builds memory for the learning algorithm, this work enables more adaptive and efficient robot movement. More recently (2022, 3 citations), Li has tackled the pressing real-world problem of hazardous gas inspection in narrow man–machine environments. Their research optimizes dynamic obstacle avoidance speed while respecting personal space constraints, making autonomous inspection safer and more practical in warehouses and other confined industrial settings. Li's contributions are particularly valuable for advancing mobile robot autonomy in safety-critical applications where human-robot interaction is unavoidable.
Research Focus
Key Achievements
Top Papers
- 1
- 2