Senbo Fu
Papers
4
Total Citations
21
H-Index
3
About
Senbo Fu is a researcher whose work lies at the intersection of numerical optimization and robotic kinematics, with a particular focus on redundancy resolution for robot manipulators. His major contributions include the development of two novel numerical algorithms—the E47 algorithm and the 94LVI algorithm—for efficiently solving inequality-and-bound constrained quadratic programming (QP) problems, as detailed in his most-cited paper (9 citations). Fu has also advanced the field by proposing feedback-type minimum-weighted-velocity-norm (FTMWVN) schemes and their acceleration-level equivalents, rigorously proven using Zhang dynamics (6 citations). A key theme in his research is establishing the equivalence between position-level and velocity-level redundancy-resolution schemes, demonstrating how different mathematical formulations can yield consistent solutions for self-motion planning of robot arms (3 citations each). His work bridges theoretical optimization with practical robotic control, offering efficient computational tools for real-time applications. While his citation counts are modest, Fu's systematic approach to proving scheme equivalences and developing robust numerical methods represents a foundational contribution to the field of robot kinematics and constrained optimization.
Research Focus
Key Achievements
Top Papers
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