Min Lai

About

Min Lai is a pioneering researcher in intelligent robotic manufacturing, specializing in 3D vision-guided automation for welding and assembly processes. His work addresses a critical bottleneck in industrial robotics: the labor-intensive programming of welding trajectories. Lai’s most influential contribution, his 2022 paper on seam extraction and path planning for medium-thickness plate structural parts, has garnered 97 citations, establishing a foundational method that eliminates manual teaching by leveraging 3D point cloud data. He further advanced this field with a 2023 study on plane weld trajectory planning (35 citations) and a 2024 feature extraction approach over workpiece point clouds (19 citations), collectively reducing reliance on geometric assumptions in weld detection. Beyond welding, Lai developed an automatic riveting method using the improved EDCircles algorithm, demonstrating versatility in robotic assembly. His work has significant implications for smart manufacturing, enabling faster, more flexible production lines. With over 150 total citations and growing recognition, Lai is shaping the future of autonomous robotic systems in heavy industry.

Research Focus

Key Achievements

Top Papers

1. 1
   A novel seam extraction and path planning method for robotic welding of medium-thickness plate structural parts based on 3D vision

   97 citations · 2022
2. 2
   A point cloud-based welding trajectory planning method for plane welds

   35 citations · 2023
3. 3
   A Feature Extraction Approach Over Workpiece Point Clouds for Robotic Welding

   19 citations · 2024
4. 4
   An automatic riveting method for robots based on the improved EDCircles algorithm

   6 citations · 2023

Key Collaborators

Contact & Links