Papers
14
Total Citations
250
H-Index
9
About
Hangjie Mo is a pioneering researcher at the intersection of surgical robotics, computer vision, and autonomous systems, with a focus on minimally invasive surgery (MIS) and continuum robot control. His work addresses some of the most pressing challenges in robotic surgery, including 3D scene reconstruction, laparoscopic navigation, and intelligent collision avoidance. Mo's most cited contribution, a stereo dense scene reconstruction framework for laparoscopic navigation (47 citations), exemplifies his ability to bridge perception and surgical autonomy. His development of 3D collision avoidance methods (34 citations) and laser-assisted endoscopic automation (34 citations) further demonstrates his commitment to enhancing surgical safety and precision. Notably, Mo has made significant strides in continuum robot control, pioneering model-predictive deformation strategies and data-efficient learning approaches that enable robots to operate effectively in constrained, unstructured environments. His more recent work on monocular depth estimation using self-supervised learning and robot kinematics reflects a forward-looking integration of multimodal data for surgical navigation. With a growing body of work totaling over 230 citations, Mo's research is shaping the future of human-robot collaboration in surgery, making procedures safer, smarter, and more autonomous.
Research Focus
Key Achievements
Top Papers
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- 3Task Autonomy of a Flexible Endoscopic System for Laser-Assisted Surgery34 citations · 2022
- 4Automated 3-D Deformation of a Soft Object Using a Continuum Robot31 citations · 2020
- 5Review of Human–Robot Collaboration in Robotic Surgery24 citations · 2024
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