Songlin Gou

Southwest University of Science and Technology

Papers

Total Citations

H-Index

About

Songlin Gou is a researcher whose work lies at the intersection of robotics, calibration, and teleoperation—fields critical to advancing autonomous and human-guided robotic systems. Gou’s major contributions include developing an improved hand-eye calibration method that leverages 3D position information to unify robot and measurement coordinate systems, effectively mitigating errors caused by geometric parameter inaccuracies and joint angle drift. This work, cited three times, addresses a fundamental challenge in robotic precision. In parallel, Gou proposed a novel workspace mapping method for master-slave isomeric robots, introducing a position-joint hybrid mapping approach based on edge drifting. This technique enhances the safety and stability of teleoperation systems by dividing the operation into distinct phases, ensuring smoother and more reliable control. With each of these key papers garnering three citations, Gou’s research is steadily gaining recognition for its practical impact on robot accuracy and operator safety. By tackling both calibration and mapping challenges, Songlin Gou is contributing foundational solutions that support more robust and intuitive robotic systems, making their work of particular interest to students and researchers in robotics and human-machine interaction.