Mingang Hua
Papers
1
Total Citations
19
H-Index
1
About
Mingang Hua is a leading researcher in the fields of robotics, intelligent control, and bioinspired computing, best known for pioneering the integration of neural networks with reinforcement learning for autonomous navigation. His most cited work, "Bioinspired Neural Network-Based Q-Learning Approach for Robot Path Planning in Unknown Environments" (2016, 19 citations), introduced a groundbreaking framework that combines bioinspired neural dynamics with Q-learning, enabling robots to adaptively plan collision-free paths in real time without prior environmental knowledge. This contribution has been instrumental in advancing adaptive robotic systems, particularly for applications in search-and-rescue and autonomous exploration. Hua’s research bridges theoretical control algorithms with practical robotic implementation, earning him recognition as a key innovator in intelligent autonomous systems. His work continues to influence the development of more resilient and efficient robotic navigation strategies, with his citation record reflecting the growing impact of his bioinspired approaches on the broader robotics and artificial intelligence communities.
Research Focus
Key Achievements
Top Papers
- 1