Papers
9
Total Citations
113
H-Index
7
About
Lingxue Ren’s research lies at the intersection of medical robotics, teleoperation, and inverse kinematics, with a focus on snake-like robots for minimally invasive surgery. Her major contributions include the development of the Deeply-learnt Damped Least-Squares (DL-DLS) method, a novel approach that leverages deep learning to solve the inverse kinematics problem for highly redundant snake-like robots, enabling real-time, accurate control. This work, her most cited with 30 citations, addresses a critical bottleneck in surgical robotics. She also pioneered a non-iterative geometric approach for inverse kinematics, offering a computationally efficient alternative to traditional iterative methods, and designed a master-slave control system with workspaces isomerism to improve teleoperation fidelity. Her research extends to robotic catheter systems with motion and force feedback for vascular surgery, and a Fuzzy-PD model for enhanced master-slave tracking. Notably, she applied her snake-like robot designs to radiosurgery of gastrointestinal tumors, demonstrating a clear path from theory to clinical application. With over 100 combined citations across her key papers, Ren’s work is foundational for advancing the dexterity, control, and clinical viability of snake-like surgical robots.
Research Focus
Key Achievements
Top Papers
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- 5A Fuzzy-PD model for master-slave tracking in teleoperated robotic surgery12 citations · 2016
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- 8Research on the lower limb gait rehabilitation7 citations · 2014
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