Shuai Tan
Papers
1
Total Citations
7
H-Index
1
About
Shuai Tan is a researcher in robotics and optimization algorithms, with a focus on enhancing the motion efficiency and stability of robotic systems. His work centers on developing advanced metaheuristic algorithms for joint trajectory planning, a critical area that directly impacts robot working quality and stationarity. Tan's most notable contribution is the Slime Mould Whale Optimization Algorithm (SMWOA), a hybrid approach that addresses the limitations of conventional trajectory optimization methods, such as slow convergence and weak global search ability. His seminal 2022 paper on this topic has garnered 7 citations, reflecting its relevance in improving robotic performance. By integrating biological inspiration from slime mould and whale behaviors, Tan offers a novel solution for smoother, more efficient robot motion. His research is particularly valuable for students and engineers seeking to advance automation and intelligent control systems, bridging the gap between theoretical optimization and practical robotics applications.
Research Focus
Key Achievements
Top Papers
- 1