Che-Cheng Chang
Papers
2
Total Citations
32
H-Index
2
About
Che-Cheng Chang is a researcher at the forefront of autonomous systems, specializing in deep reinforcement learning, unmanned aerial vehicles (UAVs), and intelligent ground vehicle control. His most cited work, "Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning" (2020, 22 citations), introduces a novel framework that enhances drone autonomy for critical applications such as exploration, rescue, and surveillance. By integrating reinforcement learning with precise flight dynamics, Chang significantly improves the accuracy and reliability of autonomous drone maneuvers, addressing key challenges in real-world deployment. In his subsequent study, "Autonomous Driving Control Based on the Perception of a Lidar Sensor and Odometer" (2022, 10 citations), he advances unmanned ground vehicle (UGV) navigation by fusing lidar and odometry data for robust path planning and obstacle avoidance. This work demonstrates his ability to bridge perception and control in complex environments. Chang’s contributions are pivotal for the next generation of autonomous robotics, offering practical solutions that enhance safety and efficiency in both aerial and ground platforms. His research continues to inspire innovations in smart transportation and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2