Last updated: March 27th 11:00AM

The project focuses on virtual fixture guidance for robotic-assisted surgeries.

• Students: Zhaoshuo (Max) Li, Anurag Madan
• Mentor(s): Nirav Patel, Mahya Shahbazi

Many anatomies are very fragile, such as retina tissue and facial nerve. Therefore, it is essential to protect the patient while operating to reduce the surgical complication. An example could be Mastoidectomy, where facial nerves are close to the drilling location.

Virtual fixtures are used in robot-guided surgery to constraint the users' motion in sensitive areas. However, complex virtual fixtures are difficult to implement, and can be computationally very expensive, causing lag and/or instability.

The goal of this project is to implement simple and complex virtual fixtures using optimized data structures for the Galen Mark I robot.

• Minimum: (Expected by April 20th)
  1. Framework for simple virtual fixtures. Fixtures would be
     1. Plane constraints​
     2. Insertion along an axis​

• Expected: (Expected by May 1)
  1. Framework for more complex virtual fixtures like constraints for 3D surfaces​
  2. Framework for automated switching of modes from virtual fixtures to free motion depending on location of tool tip

• Maximum: (Expected by May 9)
  1. Framework to register anatomy and compute virtual fixture constraints based on CT scan data

Our main task is implementing simple and surface virtual fixtures for hand-over-hand control of the Galen robot.

1. Simple virtual fixtures
2. Surface virtual fixtures
   1. Approach 1: Break the surface into multiple planes, and add a plane constraint for all planes. This works well when the number of planes is less than 1000, and the shape of the surface itself is concave. A convex surface gives some regions which are not optimizable, as the end-effector of the robot can be below a plane and still above the surface.
   2. Approach 2: Break the surface into multiple meshes using MATLAB, and implement a closest point constraint instead of a plane constraint, finding the closest points inside or on all meshes inside a bounding box. We also dynamically update the active meshes using a co-variance tree.

1. Dependency: Access to Galen Mark I robot hardware
   • Date: Mar 27
   • Status: Resolved
2. Dependency: Access to Galen Mark I robot code
   • Date: Mar 28
   • Status: Resolved
3. Dependency: Access to CT data (if necessary)
   • Date: May 5
   • Status: In progress
4. Dependency: Access to phantom skull (if necessary)
   • Date: May 5
   • Status: In progress

1. Milestone name: Jacobian and FK correction
   • Planned Date: Apr 1
   • Expected Date: Apr 3
   • Status: Completed
2. Milestone name: Simple virtual fixture implementation
   • Planned Date: Apr 7
   • Expected Date: Apr 10
   • Status: Completed
3. Milestone name: Surface virtual fixture logic
   • Planned Date: Apr 12
   • Expected Date: Apr 20
   • Status: Completed
4. Milestone name: Surface virtual fixture implementation
   • Planned Date: Apr 20
   • Expected Date: May 1
   • Status: Completed

• Project Plan
  • Project plan presentation
• Project Background Reading
  • See Bibliography below for links.
  • [1] Software framework for complex robot control.
  • [2] Constraint Optimization.
  • [3] Covariance Tree.
• Project Checkpoint
  • Project checkpoint presentation
• Paper Seminar Presentations
  • Paper Seminar Presentation Zhaoshuo Li
  • Paper Review Summary Zhaoshuo Li
  • paperpresentation_anuragmadan.pdf
  • anurag_madan_paper_review_cis.pdf
• Project Final Presentation
  • PDF of Poster
• Project Final Report
  • Final Report
  • links to any appendices or other material

* here list references and reading material

* [1] Chalasani, Preetham, et al. “A Computational Framework for Complementary Situational Awareness (CSA) in Surgical Assistant Robots.” 2018 Second IEEE International Conference on Robotic Computing (IRC). IEEE, 2018.

* [2] A. Kapoor. Motion Constrained Control of Robots for Dexterous Surgical Tasks. PhD thesis, Johns Hopkins University, September 2007.

* [3] J. P. Williams, R. H. Taylor, and L. B. Wolff, “Augmented KD techniques for accelerated registration and distance measurement of surfaces,” in Computer Aided Surgery: Computer-Integrated Surgery of the Head and Spine, Sep. 1997, pp. 1–21

Here give list of other project files (e.g., source code) associated with the project. If these are online give a link to an appropriate external repository or to uploaded media files under this name space (2019-03).