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About

Weiji Wang’s research lies at the intersection of intelligent control systems, autonomous navigation, and robotics, with a particular focus on unmanned ground vehicles (UGVs) and legged robots. His work addresses critical challenges in motion control and trajectory tracking in dynamic, cluttered environments. Wang’s most influential paper, “Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller” (2017, 26 citations), introduces a fuzzy inference system (FIS) that enables UGVs to navigate safely and efficiently without human intervention. He further advanced the field with his 2015 study on Levenberg-Marquardt optimised neural networks for trajectory tracking, demonstrating how fractional-order proportional-integral-derivative (FOPID) controllers can be enhanced through artificial neural networks. In 2019, Wang extended his expertise to quadruped robots, designing a stable and intelligent controller that balances locomotion and adaptability. His most recent work, “OFVO: A Visual Odometry Designed for Motion Trajectory Estimation of Autonomous Vehicles” (2024), marks a shift toward visual odometry, aiming to improve trajectory estimation for autonomous driving. With over 55 citations across his key publications, Wang’s contributions are shaping the future of autonomous vehicle control and robotic mobility.