Papers
4
Total Citations
77
H-Index
4
About
Longjun Wang is a leading researcher in intelligent robotics and autonomous navigation, with a particular focus on vision-guided systems for automated guided vehicles (AGVs). His work addresses critical challenges in mobile robot perception, including non-uniform illumination, image noise, and limited field-of-view constraints. Wang’s most cited paper, “SVM-based image partitioning for vision recognition of AGV guide paths under complex illumination conditions” (2019, 36 citations), introduces a novel machine learning approach to robust path detection in difficult visual environments. He further advanced the field with “Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace” (2019, 21 citations), which tackles real-world obstacles like occlusion and stripe damage. His earlier work on an automated guided mechatronic tractor (2015, 16 citations) demonstrates practical applications in heavy-duty robotic vehicles. More recently, Wang has explored receding-horizon control strategies for smooth trajectory tracking at high speeds (2020). With over 77 total citations, his contributions are essential for researchers and engineers working on vision-based navigation in manufacturing, logistics, and autonomous transportation systems.
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