Automating Multi-Throw Suturing
This project is maintained by BerkeleyAutomation
To explore supervised automation of multi-throw suturing in Robot-Assisted Minimally Invasive Surgery, we present a novel mechanical needle guide design and an optimization framework to optimize needle size, needle trajectory and control parameters for two arms using sequential convex programming. We also develop a model-based needle tracking system to achieve closed loop control. We show that in comparison with the standard 8mm needle driver, our Jaw-mounted Needle Guide (JNG) improves accuracy of needle orientation by 3x on average in the presence of needle orientation noise of up to 30 degrees in either axis, and reduces the need for needle re-alignment before subsequent needle insertion. Use of real-time needle tracking can estimate pose with a standard deviation of 2.94mm and 6.8 degrees. We demonstrate our framework on a 4-throw suturing task on a da~Vinci Research Kit using tissue phantoms for skin and subcutaneous fat. We evaluate suturing performance based on success rate and compare completion time with suturing demonstrations in JIGSAWS dataset. We evaluate effects of pose constraints on the suture path with respect to path length and tissue trauma. Our results indicate that dVRK can perform 4-throw suturing at ~1/3 of human speed at a success rate of 50% for the 4-throw task, successfully completing 86% of suture throws attempted.
Animesh Garg, David V. Gealy, Stephen McKinley, Yiming Jen
PIs: Ken Goldberg
Please Contact Animesh Garg at email@example.com