Lingjie Xu
Papers
1
Total Citations
11
H-Index
1
About
Lingjie Xu is a robotics researcher whose work centers on the kinematic control and optimization of redundant robot manipulators. Their most significant contribution is the development of the Feedback-Added Pseudoinverse-Type Balanced Minimization (FPBM) scheme, introduced in a 2019 paper that has garnered 11 citations. This innovative approach elegantly combines the minimum acceleration norm (MAN) solution with the weighted minimum velocity norm (WMVN) to achieve superior motion planning and control. By integrating feedback mechanisms into the pseudoinverse framework, Xu’s work addresses critical challenges in balancing competing performance criteria—such as joint velocity and acceleration limits—during real-time robotic operations. This research has practical implications for industrial automation, surgical robotics, and any application requiring precise, smooth, and efficient manipulator motion. While the citation count reflects a growing recognition within the specialized field of robotic kinematics, Xu’s methodology stands out for its mathematical rigor and direct applicability to redundant systems, offering a robust solution that improves upon traditional pseudoinverse-based approaches. Their work continues to influence researchers seeking to enhance the dexterity and reliability of robotic manipulators in complex tasks.
Research Focus
Key Achievements
Top Papers
- 1