Papers
1
Total Citations
6
H-Index
1
About
Yali Lv is a researcher primarily focused on intelligent path planning and autonomous navigation for aerospace vehicles, with a particular emphasis on hypersonic cruise vehicles (HCVs) operating in near-space environments. Their most notable contribution is the development of a Q-learning-based dynamic path planning framework that enables HCVs to safely avoid unknown threats from ground or space—a critical challenge in modern aerospace engineering. This work, published in 2020 with 6 citations, bridges reinforcement learning techniques with real-time trajectory optimization, offering a novel solution for vehicles flying in contested or uncertain airspace. By adapting robotic path planning methods to the high-speed, high-altitude regime of hypersonic flight, Lv addresses a key gap in autonomous navigation for defense and space applications. Their research demonstrates how machine learning can enhance the survivability and mission success of advanced aerial platforms, making their work valuable for engineers and researchers working on intelligent flight control systems, autonomous drones, and next-generation aerospace vehicles.
Research Focus
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