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About

Lianming Wang is a leading researcher in intelligent robotics and computer vision, with a focus on advancing autonomous systems through multi-sensor perception and deep learning. His work spans three key areas: human-robot interaction via gait analysis, underwater imaging, and bio-inspired robotics. Wang’s most cited paper, "Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network" (2020, 33 citations), introduces a novel neural network approach for precise gait phase recognition, critical for rehabilitation robots and prosthetic control. In underwater robotics, his 2024 study "Underwater Image Enhancement via Modeling White Degradation" (11 citations) tackles light absorption and scattering challenges, improving visual perception for aquatic robots. Wang also contributes to ethorobotics with "Pose Estimation-Based Visual Perception System for Analyzing Fish Swimming" (2024, 9 citations), enabling detailed movement analysis. His earlier work on fuzzy color segmentation for robot vision (2015, 6 citations) and AGV navigation using AprilTags (2019, 3 citations) demonstrates sustained innovation in autonomous guidance. Notably, his 2024 paper "LUO-V2 and MSPerception" introduces a multi-fin robotic fish with an integrated multi-sensor system, showcasing his ability to merge perception algorithms with physical robot design. With over 60 total citations, Wang’s research directly impacts rehabilitation technology, marine exploration, and autonomous navigation.