Mingliang Mei

Fujian Polytechnic of Information Technology

Papers

Total Citations

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About

Mingliang Mei is a leading researcher in field robotics, specializing in terrain classification and adaptive navigation for wheeled and tracked mobile robots. His work addresses a critical challenge: enabling autonomous robots to perceive and respond to their environment through vibration-based sensing and kinematic modeling. In his highly cited 2019 paper (43 citations), Mei systematically compares methods for vibration-based terrain classification in wheeled robots with shock absorbers, demonstrating how robot-terrain interaction signals can be used to enhance operational safety and efficiency. His 2018 study (15 citations) tackles the persistent problem of slippage in skid-steering tracked robots, introducing a terrain-adaptive method for estimating instantaneous centers of rotation to improve kinematic model accuracy. These contributions are foundational for robots operating in unstructured outdoor environments, where traditional models fail due to unpredictable ground conditions. Mei’s research bridges the gap between theoretical kinematics and practical field deployment, offering robust solutions for autonomous navigation in agriculture, search-and-rescue, and planetary exploration. His work continues to influence the design of more resilient and perceptive mobile robots.