Papers
1
Total Citations
10
H-Index
1
About
Mingye Yang is a rising researcher in the fields of autonomous systems, robotics, and intelligent control, with a particular focus on unmanned aerial vehicle (UAV) mission planning. Yang’s most cited work, “UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment” (2023), has already garnered 10 citations, signaling growing recognition in the community. This paper addresses a critical challenge in the high-end robotics industry: enabling UAVs to autonomously navigate and plan tasks in rapidly changing, unpredictable environments. By integrating reinforcement learning with path planning, Yang’s research offers a robust framework for real-time decision-making, moving beyond static, pre-programmed routes. This contribution is especially vital for applications in disaster response, surveillance, and logistics, where adaptability is paramount. Yang’s work stands out for its practical, industry-oriented approach, bridging the gap between theoretical reinforcement learning algorithms and real-world robotic deployment. As the demand for intelligent, autonomous drones continues to surge, Yang’s research provides a foundational methodology for safer, more efficient UAV operations. With a clear trajectory of impact, Mingye Yang is establishing themselves as a key innovator in the next generation of autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1