Weirong Liu
Papers
8
Total Citations
121
H-Index
4
About
Weirong Liu is a leading researcher in mobile robotics, specializing in path planning, trajectory generation, and autonomous navigation in complex environments. Their work centers on developing intelligent optimization algorithms—such as chaotic adaptive particle swarm optimization (CAPSO), mutual learning ant colony optimization (MuL-ACO), and adaptive unscented particle filters—to enable robots to move smoothly and safely through static, uneven, or dynamic settings. A standout contribution is their cubic spline interpolation-based path planning method, which uses CAPSO to ensure smooth, collision-free robot motion; this work has garnered 76 citations, reflecting its significant impact on the field. Liu has also advanced visual servoing for nonholonomic robots under field-of-view constraints and proposed unified optimization techniques for real-time trajectory generation with kinodynamic constraints. More recently, they have explored AI-enabled bumpless transfer control for legged robots with hybrid energy storage systems, addressing critical challenges in energy efficiency and motion stability. With over 120 total citations across their most-cited papers, Liu’s research consistently bridges theoretical optimization with practical robotic applications, making their work essential reading for students and engineers seeking robust, real-world solutions in autonomous navigation and robot control.
Research Focus
Key Achievements
Top Papers
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- 5A Chaotic Adaptive Particle Swarm Optimization for Robot Path Planning3 citations · 2019
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