Liguo Song
Papers
4
Total Citations
53
H-Index
3
About
Liguo Song is a leading researcher in bio-inspired soft robotics and triboelectric nanogenerator (TENG)-based tactile sensing, with a focus on underwater applications. His work bridges the gap between biological sensory systems and engineered devices, creating highly sensitive, self-powered sensors that mimic the tactile capabilities of marine organisms. Song’s major contributions include the development of a palm-like 3D tactile sensor for underwater robot grippers, which detects both vertical and shear forces—enabling precise object identification and grasp in low-visibility, high-noise ocean environments, inspired by sea otters. He has also designed an octopus-inspired soft gripper with embedded triboelectric sensors for underwater target recognition, and a bionic whisker sensor that replicates seal vibrissae to measure flow velocity and detect collisions. His most-cited paper (2024) has garnered 21 citations, reflecting growing interest in this niche. Song’s innovative use of liquid-metal electrodes and TENG technology enhances sensor durability and power efficiency, positioning his work at the forefront of marine robotics and human-machine interaction. His achievements promise to revolutionize underwater exploration and manipulation.
Research Focus
Key Achievements
Top Papers
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