Ye Tang
Papers
1
Total Citations
19
H-Index
1
About
Ye Tang is a leading researcher in the field of industrial robotics, with a primary focus on enhancing robot accuracy through advanced calibration and error modeling. Their most significant contribution is the development of a comprehensive on-load calibration method that integrates a unified kinetostatic error model with Gaussian process regression, a breakthrough that addresses the critical challenge of absolute accuracy degradation caused by kinematic parameter errors, joint compliance, and other nonlinear factors. This work, published in 2024 and already garnering 19 citations, demonstrates Tang’s ability to blend theoretical rigor with practical application, offering a robust solution for real-world manufacturing environments. By tackling the complex interplay between static and dynamic errors under load conditions, Tang has provided a pathway for industrial robots to achieve higher precision in tasks like machining and assembly, directly impacting productivity and quality control. Their research is notable for its systematic approach, combining physical modeling with data-driven techniques, and has positioned Tang as a key innovator in the ongoing effort to bridge the gap between robot flexibility and accuracy. For students and researchers, Tang’s work exemplifies how interdisciplinary methods can solve longstanding industrial challenges.
Research Focus
Key Achievements
Top Papers
- 1