Shaoqian Tian
Papers
1
Total Citations
23
H-Index
1
About
Shaoqian Tian’s research centers on trajectory planning and motion optimization for robotic mechanisms, with a particular focus on improving stability and precision in pointing systems. His major contribution lies in developing a novel hybrid interpolation algorithm combined with the NSGA-II multi-objective optimization framework, which enables continuous jerk profiles and reduces computational complexity compared to traditional higher-order polynomial or B-spline methods. This work, published in 2020, has garnered 23 citations, reflecting its relevance to researchers working on smooth, efficient motion control in robotics and automation. Tian’s approach addresses a critical challenge in trajectory optimization—balancing motion smoothness with computational efficiency—making it valuable for applications in aerospace pointing mechanisms and industrial manipulators. His research demonstrates a practical integration of advanced interpolation techniques and evolutionary algorithms, offering a scalable solution for real-time trajectory planning. For students and researchers in robotics and control systems, Tian’s work provides a clear example of how hybrid methods can enhance both performance and computational tractability in complex motion tasks.
Research Focus
Key Achievements
Top Papers
- 1