Papers
10
Total Citations
160
H-Index
7
About
Ran Huang is a robotics and autonomous systems researcher whose work spans intelligent control, mobile robot navigation, simultaneous localization and mapping (SLAM), and multi-robot coordination. His most influential contribution lies in the control of snake robots, where his 2017 paper on neuro-optimal control using approximate dynamic programming and neural networks garnered 72 citations, establishing him as a notable voice in underactuated robotic systems. Complementing this, his investigations into central pattern generator-based gait transitions and deterministic learning-based tracking control further demonstrate his commitment to biologically inspired locomotion. Beyond snake robotics, Huang has made meaningful advances in autonomous navigation, developing deep reinforcement learning frameworks that enable mobile robots to navigate effectively in unknown and dynamic environments. His work on semantic SLAM pushes the boundaries of scene understanding by tackling complex data association challenges often overlooked by prior research. He has also contributed to practical applications, including LiDAR-based point cloud segmentation for autonomous vehicles, multi-sensor assistive navigation systems for the visually impaired, and task allocation strategies for warehouse robot fleets. With a cumulative citation profile reflecting growing influence across diverse robotics subfields, Huang represents a versatile and productive contributor to modern intelligent robotics research.
Research Focus
Key Achievements
Top Papers
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- 7Smooth transition of the CPG-based controller for snake-like robots8 citations · 2017
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- 9Robust Indoor Visual-Inertial SLAM with Pedestrian Detection4 citations · 2021
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