Max Robotics — Global Service Robot Directory + US Certification

About

Jinglun Liang is a leading researcher in industrial robotics, specializing in precision positioning, digital twin technology, and intelligent motion planning. His most impactful work addresses the critical challenge of positioning error in robotic arms—a problem that degrades performance over time due to transmission wear. Liang pioneered a low-cost digital twin-driven compensation method (29 citations), creating virtual replicas of physical robots to optimize engineering performance and enable intelligent maintenance. His follow-up work on 3-D position information mutuality (20 citations) further advanced this approach, offering an affordable alternative to expensive high-precision systems. Liang has also made notable contributions to collision-free motion planning for dual manipulators using recurrent neural networks (11 citations), solving real-time control challenges in overlapping workspaces. Earlier in his career, he conducted dynamic analysis of flexible parallel robots (7 citations) and developed a novel XY-Theta alignment stage for screen printing (4 citations), demonstrating his versatility across robotic systems. His research on motion tracking using RGB-D cameras and IMUs (5 citations) has applications in human-robot interaction. Liang’s work bridges the gap between theoretical robotics and practical industrial deployment, with his digital twin methods offering scalable solutions for precision manufacturing.