Guolei Wang
Papers
8
Total Citations
92
H-Index
5
About
Guolei Wang is a leading researcher in intelligent robotics and manufacturing automation, with a focus on large-scale industrial applications such as robotic drilling, spray painting, and autonomous navigation. His work addresses critical challenges in precision and efficiency for complex environments. Wang’s major contributions include developing a normal direction measurement and optimization method using dense 3D point clouds for robotic drilling, which enables online correction of tool and force errors—cited 26 times. He also pioneered fringe pattern-based plane-to-plane visual servoing for spray path planning, overcoming deformation issues to ensure paint film uniformity (21 citations). In multi-robot systems, Wang introduced an option-based multi-agent reinforcement learning approach for cooperative painting, achieving conflict-free task allocation (18 citations). His high-precision vision localization system for autonomous guided vehicles in dusty industrial settings (7 citations) further demonstrates his impact on Industry 4.0. With additional work on coating thickness modeling and joint torque control for collaborative robots, Wang’s research has accumulated over 90 citations, showcasing his influence in advancing robotic precision and autonomy for real-world manufacturing.
Research Focus
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