Papers
9
Total Citations
202
H-Index
6
About
Yixuan Guo is a robotics researcher whose work centers on the precision calibration of industrial robots, with growing interests in autonomous robot navigation. Guo has made substantial contributions to the field of robot kinematic and geometric error calibration, developing innovative methods that improve the accuracy and efficiency of six-degree-of-freedom serial industrial robots. Among their most impactful contributions is a novel framework for identifying joint-dependent geometric errors across multiple identification spaces, which has garnered 56 citations since its 2021 publication, alongside complementary work on elasto-geometrical calibration and distance-based kinematic parameter estimation. A recurring theme in Guo's research is the democratization of calibration technology—pioneering the use of affordable laser displacement sensors as alternatives to costly metrology equipment, making high-precision robot calibration more accessible to industry practitioners. Their development of non-contact R-test calibration methods has further broadened the toolkit available to robotics engineers. More recently, Guo has expanded into dynamic path planning for wheeled humanoid robots, addressing real-world challenges such as slippery workshop floor conditions using nature-inspired algorithms. With a total citation count exceeding 200, Guo's body of work represents a meaningful and practical advancement in industrial robot performance optimization.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9