Leijie Lai

Shanghai University of Engineering Science

Papers

Total Citations

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About

Leijie Lai is a researcher advancing the frontiers of robotic perception and adhesion, with key contributions in vision-based measurement and wall-climbing robot design. His most cited work, "Robot line structured light vision measurement system: light strip center extraction and system calibration" (2021, 13 citations), tackles the challenge of rapid, online 3D measurement of complex objects. By modeling the robot vision system and refining light strip extraction and calibration methods, Lai enables high-precision, real-time inspection—critical for industrial automation and quality control. In his more recent study, "Derivation and Experimental Validation of Multi-Parameter Performance Optimization of Magnetic Adhesion Unit of Wall-Climbing Robot" (2025, 3 citations), he addresses a core bottleneck in robotics: reliable adhesion on complex vertical surfaces. Through systematic derivation and experimental validation, Lai optimizes magnetic adhesion parameters, paving the way for safer, more versatile robots in chemical tank maintenance, high-altitude operations, and infrastructure inspection. His work bridges theoretical modeling and practical deployment, earning recognition for its direct industrial relevance. Lai’s research is essential reading for students and engineers seeking to understand how robots can see and stick with greater accuracy and reliability.