Papers
3
Total Citations
19
H-Index
3
About
Linjing Liao is a robotics researcher whose work centers on the dynamic modeling and simulation of modular and reconfigurable robotic systems. Her primary contributions lie in advancing the application of Kane’s dynamics method—a computationally efficient alternative to traditional Lagrangian approaches—for multi-degree-of-freedom (DOF) modular robots. In her most cited work (8 citations), she developed a Kane dynamics model for a 5-DOF modular industrial robot, validated through Adams simulations, enabling more accurate joint parameter inputs. She further extended this by comparing Lagrange and Kane methods for a 4-DOF robot (6 citations), demonstrating the practical advantages of Kane modeling in simulation environments. Notably, her 2017 paper (5 citations) proposed an improved Kane dynamic model for a 7-DOF reconfigurable modular robot, enhancing model precision and introducing three progressive joint designs suitable for industrial, space, and special-purpose robots. With a cumulative impact of over 19 citations across her key works, Liao’s research provides foundational tools for designing and controlling modular robots with greater accuracy and adaptability. Her work is particularly valuable for students and engineers exploring efficient dynamics modeling in reconfigurable robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3