Ninghe Lu

About

Ninghe Lu is a researcher specializing in the modeling, calibration, and motion control of hybrid and industrial robots. Their work focuses on improving the accuracy and performance of robotic systems through innovative theoretical and practical approaches. A key contribution is the development of an efficient model-free calibration method for a 5-degree-of-freedom hybrid robot, which bypasses complex error modeling to enhance pose accuracy—a critical issue limiting hybrid robot applications (10 citations). Lu has also advanced the field by creating comprehensive dynamic models for hybrid robots, essential for both design and application (6 citations), and by studying the motion performance of 2UPR-1RPS/2R hybrid robots through integrated kinematics, dynamics, and stiffness modeling (2 citations). Additionally, their work on path planning for rail-mounted industrial robots, utilizing analytical inverse kinematics for continuous-trajectory tracking, demonstrates practical solutions for redundant robotic work cells (2 citations). With a growing body of work that bridges theoretical modeling and real-world application, Lu is making notable strides in enhancing the capabilities of hybrid and industrial robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Efficient Model-Free Calibration of a 5-Degree of Freedom Hybrid Robot

   10 citations · 2022
2. 2
   Dynamic Modeling and Simulation of a Hybrid Robot

   6 citations · 2022
3. 3
   Motion Performance Study of 2UPR-1RPS/2R Hybrid Robot Based on Kinematics, Dynamics, and Stiffness Modeling

   2 citations · 2023
4. 4
   Path planning based on the analytical inverse kinematics of the rail-mounted industrial robot for continuous- trajectory tracking

   2 citations · 2020

Key Collaborators

Contact & Links