Abstract
Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation which is associated with reduced patient morbidity. DALK has been explored as a potential application of robot microsurgery because the small scales, fine control requirements, and difficulty of visualization make it very challenging for human surgeons to perform. We address the problem of modelling the small scale interactions between the surgical tool and the cornea tissue to improve the accuracy of needle insertion, since accurate placement within 5% of target depth has been associated with more reliable clinical outcomes. We develop a data-driven autoregressive dynamic model of the tool-tissue interaction and a model predictive controller to guide robot needle insertion. In an ex vivo model, our controller significantly improves the accuracy of needle positioning by more than 40% compared to prior methods.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1526-1533 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2022 |
| Externally published | Yes |
Keywords
- Medical robots and systems
- Model learning for control
- Surgical robotics: planning
ASJC Scopus subject areas
- Control and Systems Engineering
- Biomedical Engineering
- Human-Computer Interaction
- Mechanical Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Control and Optimization
- Artificial Intelligence