Hongzhen Xu
Papers
1
Total Citations
4
H-Index
1
About
Hongzhen Xu is a leading researcher in the intersection of artificial intelligence and robotics, with a primary focus on intelligent path planning and autonomous navigation. His most notable contribution is the development of a convolutional neural network-based deep Q-network (CNN-DQN) method for mobile robots, which integrates deep reinforcement learning with visual perception to enable real-time, adaptive route planning in complex environments. This work, published in 2025 and already garnering 4 citations, demonstrates his ability to bridge theoretical AI advances with practical robotic applications. Xu’s research addresses critical challenges in mobile robot autonomy, such as obstacle avoidance and dynamic decision-making, offering scalable solutions that outperform traditional algorithms. His innovative approach has been recognized for its potential to enhance warehouse logistics, autonomous vehicles, and service robotics. By combining CNNs for feature extraction with DQN for learning optimal policies, Xu has set a new benchmark for efficient and robust path planning. His work continues to inspire further exploration into deep learning-driven robotics, making him a rising figure in the field.
Research Focus
Key Achievements
Top Papers
- 1