Chenliang Lin
Papers
1
Total Citations
13
H-Index
1
About
Chenliang Lin is a researcher specializing in robotics, artificial intelligence, and autonomous navigation systems. His primary contributions lie in advancing deep reinforcement learning techniques for mobile robot obstacle avoidance and path planning. In his most cited work, "Obstacle avoidance navigation method for robot based on deep reinforcement learning" (2022, 13 citations), Lin addresses critical limitations in traditional D3QN-based navigation algorithms, including sparse reward signals and slow neural network training speeds. He proposes the LN-D3QN algorithm, which significantly improves learning efficiency and navigation performance in indoor environments. This work demonstrates Lin's ability to bridge theoretical reinforcement learning advances with practical robotic applications. His research holds particular relevance for autonomous systems operating in complex, dynamic settings. By tackling fundamental challenges in robot navigation—such as real-time decision-making under uncertainty—Lin's contributions support the development of more intelligent, self-sufficient mobile robots. His work continues to influence researchers working at the intersection of deep learning and robotics, offering practical solutions for safer and more efficient autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1