Xiangbao Song
Papers
2
Total Citations
9
H-Index
2
About
Xiangbao Song is a robotics researcher whose work focuses on the critical intersection of motion planning, energy efficiency, and industrial automation. His primary research areas include trajectory generation, kinematic optimization, and path interpolation for robotic manipulators. Song’s most cited work, “Time-optimal and energy-efficient trajectory generation for robot manipulator with kinematic constraints” (2017, 6 citations), introduces a novel algorithm that transforms complex trajectory planning into a single-state nonlinear optimization problem using cubic spline parameterization. This approach simultaneously minimizes both time and energy consumption—a significant contribution for practical industrial applications. In his complementary study, “Tool path interpolation and redundancy optimization of manipulator” (2017, 3 citations), Song addresses the challenge of 1-DoF redundant tasks like welding and cutting. By applying B-spline interpolation and minimizing energy consumption, he provides an elegant solution for optimizing redundant manipulator performance. Though early in his career, Song’s work demonstrates a clear commitment to developing computationally efficient, real-world applicable methods that balance speed, precision, and energy savings—a vital consideration for modern sustainable manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2Tool path interpolation and redundancy optimization of manipulator3 citations · 2017