Peng-Chen Lu
Papers
2
Total Citations
29
H-Index
2
About
Peng-Chen Lu is a researcher at the forefront of autonomous drone navigation, specializing in the application of reinforcement learning (RL) and deep reinforcement learning (DRL) to enhance the precision of unmanned aerial vehicles. His work addresses a critical challenge in modern robotics: enabling drones to perform fundamental maneuvers—such as straight take-off, forward flight, and landing—with high accuracy, both indoors and outdoors. Lu’s most-cited paper, "Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning" (2020, 22 citations), introduces a DRL-based framework that significantly boosts autonomous performance, while his earlier study (2019, 7 citations) pioneered the use of ArUco markers as visual references for RL-driven flight control. These contributions have practical implications for exploration, rescue, and surveillance applications, where reliable autonomous flight is essential. By bridging reinforcement learning with real-world drone operations, Lu has laid groundwork for safer and more efficient aerial systems, earning recognition for advancing the intersection of artificial intelligence and robotics.
Research Focus
Key Achievements
Top Papers
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- 2