Haoqiang Sun
Papers
1
Total Citations
4
H-Index
1
About
Haoqiang Sun is a researcher at the forefront of intelligent robotics and deep reinforcement learning, with a primary focus on developing autonomous navigation systems for mobile robots. His most influential work introduces a novel convolutional neural network-based deep Q-network (CNN-DQN) path planning method, which integrates visual perception with decision-making to enable robots to navigate complex, dynamic environments without pre-mapped routes. This contribution addresses a critical challenge in robotics—real-time, adaptive path planning—by leveraging deep learning to process raw sensor data directly, improving both efficiency and robustness. Sun’s approach has garnered early recognition, with his seminal paper accumulating 4 citations since its 2025 publication, signaling growing interest from peers in robotics and artificial intelligence. His work stands out for its practical applicability, bridging the gap between theoretical reinforcement learning algorithms and real-world robotic deployment. By advancing CNN-DQN frameworks, Sun is shaping the next generation of autonomous systems, with potential impacts on warehouse logistics, search-and-rescue missions, and service robotics. His research continues to inspire students and engineers seeking to combine deep learning with embodied intelligence.
Research Focus
Key Achievements
Top Papers
- 1