Hongyong Yang
Papers
3
Total Citations
25
H-Index
3
About
Hongyong Yang is a leading researcher in multi-robot systems, specializing in reinforcement learning for autonomous coordination and control. His work addresses critical challenges in target hunting, formation control, and path planning, particularly in environments with obstacles. Yang’s most-cited paper (2022, 12 citations) introduces a reinforcement learning-based method for multi-robot target hunting, leveraging Markov game modeling and potential energy models to achieve efficient encirclement tasks. His earlier studies (2021, 7 and 6 citations) advance multi-robot formation and path planning using improved Q-learning algorithms, enabling leader-follower systems to navigate concave obstacles and unknown terrains. These contributions are pivotal for applications in search-and-rescue, surveillance, and industrial automation, where robust, adaptive multi-robot coordination is essential. Yang’s work stands out for integrating reinforcement learning with practical constraints like obstacle avoidance, offering scalable solutions for real-world deployment. His research has garnered attention for its innovative blend of theoretical rigor and experimental validation, making him a key figure in the evolution of intelligent multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3