Canjun Xiao
Papers
3
Total Citations
20
H-Index
2
About
Canjun Xiao is a leading researcher in the field of robotic systems, with a primary focus on pipeline inspection robotics and industrial robot calibration. His work on the "Design and Kinematic Characteristic Analysis of a Spiral Robot for Oil and Gas Pipeline Inspections" (2023, 15 citations) represents a significant contribution to infrastructure maintenance, offering a novel mechanical model that analyzes critical factors like spiral angle and normal force to enhance leak detection and prevention. More recently, Xiao has pioneered advanced computational methods for improving robotic precision. His 2025 paper on "Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network" (4 citations) introduces a groundbreaking two-stage compensation framework that addresses both geometric and non-geometric errors. He further advances this line of inquiry with "A Deep Reinforcement Learning Enhanced Snow Geese Optimizer for Robot Calibration" (2025, 1 citation), which reduces reliance on domain-specific knowledge by leveraging deep reinforcement learning. Collectively, Xiao’s work bridges mechanical design and intelligent optimization, driving progress toward more autonomous, accurate, and adaptable robotic systems for critical industrial applications.
Research Focus
Key Achievements
Top Papers
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