Ping-Lun Chung
Papers
1
Total Citations
29
H-Index
1
About
Ping-Lun Chung is a leading researcher in intelligent robotics and embedded deep learning systems. His work focuses on integrating advanced computer vision algorithms into resource-constrained robotic platforms, enabling real-time object identification and autonomous navigation. Chung’s most cited paper, "Deep learning for object identification in ROS-based mobile robots" (2018, 29 citations), demonstrates a groundbreaking approach by combining the Robot Operating System (ROS) with a Raspberry Pi-based mobile robot and the Faster R-CNN algorithm. This work significantly lowered the hardware barrier for deploying deep learning in robotics, allowing small-scale robots to perform robust object detection and control—validated through rigorous experimental tests. By bridging the gap between high-performance AI models and affordable, open-source hardware, Chung has empowered students and researchers to build intelligent, cost-effective robotic systems. His contributions are pivotal in advancing accessible robotics education and prototyping, making him a key figure in the democratization of AI-driven automation.
Research Focus
Key Achievements
Top Papers
- 1Deep learning for object identification in ROS-based mobile robots29 citations · 2018