Yihang Huang
Papers
1
Total Citations
5
H-Index
1
About
Yihang Huang is a leading researcher in multi-robot systems and artificial intelligence, with a primary focus on collaborative autonomous planning for large-scale environments. Their most notable contribution is the development of a hierarchical multi-robot coverage strategy that integrates reinforcement learning with a dense segmented Siamese network, enabling efficient and complete area coverage for tasks such as disaster search and rescue, forest fire prevention, and resource exploration. This work, published in 2024 and already garnering 5 citations, addresses the critical challenge of minimizing robot (especially drone) occupancy while maximizing coverage completion on expansive maps. Huang’s research bridges the gap between theoretical reinforcement learning algorithms and practical multi-agent coordination, offering scalable solutions for real-world emergency response and environmental monitoring. By advancing the efficiency of collaborative planning, their work has significant implications for reducing operational costs and improving response times in high-stakes scenarios. As an emerging scholar, Huang’s innovative approach to hierarchical task decomposition and neural network design marks them as a rising authority in autonomous robotics and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1