Jingxuan Lin
Papers
1
Total Citations
4
H-Index
1
About
Jingxuan Lin is a leading researcher in intelligent robotics and autonomous navigation, with a primary focus on developing advanced algorithms for mobile robot path planning in complex, dynamic environments. Lin’s most notable contribution is the proposal of a deep reinforcement Q-learning network (DRQN) based on radial neural networks, which enables robots to effectively navigate and avoid both static and dynamic obstacles in challenging ground settings. This work, published in 2024, has already garnered 4 citations, signaling its emerging impact on the field of autonomous systems. By integrating deep reinforcement learning with robust neural architectures, Lin addresses critical challenges in real-world robotic mobility, enhancing the safety and efficiency of autonomous operations. Lin’s research bridges the gap between theoretical reinforcement learning and practical robotic applications, offering scalable solutions for industries ranging from logistics to search-and-rescue. With a growing citation record and a focus on cutting-edge AI-driven control, Jingxuan Lin is establishing a reputation as an innovator in the next generation of intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1