Papers
31
Total Citations
317
H-Index
10
About
Lingtao Yu is a robotics and biomedical engineering researcher whose work sits at the intersection of surgical robotics, force sensing, and autonomous control systems for minimally invasive surgery. His research has made substantial contributions to solving one of the central challenges in laparoscopic surgical robotics: enabling precise, safe force feedback within the severe size and mechanical constraints of surgical instruments. Yu has developed innovative approaches to force sensing, including cable-tension disturbance observers, joint torque-based estimation methods, and deep learning networks such as BLSTM-MLP architectures, collectively accumulating nearly 200 citations across his most impactful works. His 2018 paper on 3D force-sensing forceps (35 citations) is particularly notable for demonstrating a practical pathway to integrating multi-dimensional force perception into compact surgical end-effectors. Beyond force sensing, Yu has pioneered forecasting kinematic algorithms and hybrid grey prediction models for autonomous laparoscopic visual window navigation, significantly reducing surgeon cognitive load during procedures. His earlier foundational work on parallel robot kinematics simulation further illustrates the breadth of his mechanical systems expertise. Yu's body of research represents a meaningful and growing influence on the development of safer, smarter, and more autonomous robotic surgical systems.
Research Focus
Key Achievements
Top Papers
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- 6Kinematics simulation and analysis of 3-RPS parallel robot on SimMechanics15 citations · 2010
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