Yuhong Huang
Papers
3
Total Citations
15
H-Index
3
About
Yuhong Huang is a pioneering researcher at the intersection of bio-inspired robotics and autonomous navigation, whose work draws direct inspiration from mammalian neural systems. Their primary research areas include biologically-inspired simultaneous localization and mapping (SLAM), energy-efficient autonomous driving, and soft robotics for dynamic locomotion. Huang’s most notable contribution is a biologically-inspired SLAM system based on LiDAR sensors, which models the rodent hippocampus to enable robots to perform autonomous navigation tasks—a paper that has garnered 7 citations and represents a significant step toward neuromorphic navigation. In parallel, Huang developed an energy-efficient lane-keeping system using 3D LiDAR and spiking neural networks (SNNs), demonstrating how brain-like computing can reduce power consumption in autonomous vehicles (4 citations). Most recently, their work on optimizing dynamic balance in a rat robot through lateral flexion of a soft actuated spine (4 citations) showcases a novel approach to quadruped locomotion, enabling disabled animals—and by extension, robots—to maintain stability with fewer limbs. Huang’s research is distinguished by its seamless integration of biological principles with practical robotics, offering transformative pathways for energy-efficient, adaptive autonomous systems.
Research Focus
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Top Papers
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