Zhongjun Xiao

Shandong Academy of Sciences, Henan University of Technology

Papers

Total Citations

H-Index

About

Zhongjun Xiao is a researcher specializing in robotics, with a focus on industrial automation and minimally invasive surgical (MIS) systems. His work bridges the gap between theoretical modeling and practical applications in robotic control and human-machine interaction. Notably, Xiao’s 2022 paper on “ABB-IRB120 Robot Modeling and Simulation Based on MATLAB” (4 citations) introduces a simulation framework for robot handling processes, addressing inefficiencies in traditional manual inspection methods for industrial sorting tasks. This contribution highlights his commitment to enhancing automation precision and supervisory control in manufacturing. Earlier, in 2014, Xiao explored “Force feedback time prediction based on neural network of MIS Robot with time delay” (2 citations), tackling critical challenges in robotic surgery—specifically, the time delays and sensory feedback gaps that hinder surgeon performance in image-guided procedures. By leveraging neural networks and virtual reality-based image prediction, his research aims to improve the stability and dexterity of surgical robots, ultimately advancing patient outcomes. Though his citation counts are modest, Xiao’s work demonstrates a thoughtful integration of simulation, neural networks, and real-world robotic systems, offering valuable insights for students and researchers in industrial robotics and medical technology.