Xing Jin
Papers
1
Total Citations
5
H-Index
1
About
Xing Jin is a robotics researcher whose work centers on trajectory planning and kinematic optimization for industrial manipulators. In his most-cited paper, "Trajectory Planning of a Six-DOF Robot Based on a Hybrid Optimization Algorithm" (2016, 5 citations), Jin addresses critical limitations in motion control for six-degree-of-freedom robots. Using a PUMA 560 spot welding robot as his testbed, he applied the standard Denavit-Hartenberg method to model forward and inverse kinematics. Recognizing the shortcomings of the ant colony algorithm, Jin developed a hybrid optimization approach that integrates elements from particle swarm optimization and genetic algorithms, significantly improving trajectory smoothness and computational efficiency. This work contributes to more precise and energy-efficient robotic motion in manufacturing settings. While his citation count is modest, Jin’s research demonstrates a practical, problem-driven approach to overcoming real-world constraints in industrial robotics—offering valuable insights for engineers seeking to enhance automation performance through algorithmic innovation.
Research Focus
Key Achievements
Top Papers
- 1