Sheng-En Huang
Papers
2
Total Citations
7
H-Index
2
About
Sheng-En Huang is a robotics and autonomous systems researcher whose work bridges the gap between edge computing, underwater exploration, and satellite communications. His primary research focuses on deploying machine learning models on resource-constrained edge platforms for real-time environmental perception and navigation. In his highly cited 2023 paper on caveline detection for autonomous underwater vehicles (AUVs), Huang pioneered the use of lightweight ML-based object detection and segmentation models to enable real-time cave mapping without relying on cloud connectivity—a critical capability for exploring GPS-denied underwater environments. This work has garnered 4 citations and represents a foundational step toward fully autonomous underwater cave exploration. Additionally, Huang contributed to satellite communications with his 2023 paper on a phased-array antenna tracking algorithm implemented on the Robot Operating System (ROS), which achieved 3 citations. This research addressed the challenge of maintaining reliable connectivity with low Earth orbiting (LEO) satellites for mobile ground platforms, advancing the vision of seamless, resilient networking for autonomous systems. Huang’s work exemplifies the integration of perception, control, and communication in real-world robotic deployments.
Research Focus
Key Achievements
Top Papers
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- 2