Papers
31
Total Citations
467
H-Index
13
About
Caihong Li is a prominent researcher specializing in mobile robotics, autonomous navigation, and intelligent path planning. With a career spanning nearly two decades, Li has made significant contributions to the fields of reinforcement learning-based navigation, chaotic motion planning, and neural network applications for robotic systems. Li's early work focused on integrating Q-learning and artificial neural networks for mobile robot path planning in dynamic, unknown environments, laying foundational groundwork that has garnered over 75 combined citations. A defining thread throughout their research is the innovative application of chaotic systems — including Lorenz, Logistic, Chebyshev, and Standard maps — to generate unpredictable yet complete coverage path planners for surveillance and special-mission robotics, collectively accumulating over 120 citations across five dedicated studies. More recently, Li has expanded into deep learning methodologies, developing LSTM-based local path planning algorithms and fusion approaches combining neural networks with reinforcement learning to overcome challenges like local deadlocks and path redundancy in complex environments. Additional contributions to swarm robotics, featuring virtual pheromone-guided foraging algorithms, demonstrate the breadth of their expertise. Li's body of work reflects a sustained commitment to advancing intelligent, adaptive robotic systems, making their research an essential reference for scholars working at the intersection of autonomous navigation and machine learning.
Research Focus
Key Achievements
Top Papers
- 1An efficient initialization approach of Q-learning for mobile robots41 citations · 2012
- 2
- 3
- 4
- 5
- 6
- 7
- 8Local Path Planning of Mobile Robot Based on Artificial Potential Field26 citations · 2020
- 9
- 10