Jingyi Xie
Papers
1
Total Citations
54
H-Index
1
About
Jingyi Xie is a leading researcher at the intersection of unmanned aerial vehicles (UAVs) and artificial intelligence, with a primary focus on autonomous navigation and deep reinforcement learning. Their most influential work, "UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy" (2020), has garnered 54 citations and represents a significant breakthrough in enabling UAVs to perform complex, real-time tracking and precision landing tasks without human intervention. By integrating machine learning with robotics, Xie has addressed critical challenges in military and civil applications, where reliable autonomous operation is essential. This research not only advances the theoretical foundations of reinforcement learning in dynamic environments but also provides practical frameworks for deploying intelligent drones in surveillance, delivery, and search-and-rescue missions. Xie’s contributions are widely recognized for bridging the gap between simulation-based AI strategies and real-world UAV performance, making them a key figure in the ongoing evolution of autonomous aerial systems. Their work continues to inspire new approaches to robotic autonomy and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1