Efficiently Calibrating Cable-Driven Surgical Robots with RGBD Fiducial Sensing and Recurrent Neural Networks
| Autor: | Danyal Fer, Minho Hwang, Thomas P. Low, Samuel Paradis, Brijen Thananjeyan, Jeffrey Ichnowski, Ken Goldberg, Daniel Seita |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Computer science Computer Vision and Pattern Recognition (cs.CV) Biomedical Engineering Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Tracking error Computer Science - Robotics Artificial Intelligence Computer vision Data collection business.industry Mechanical Engineering 05 social sciences 050301 education 021001 nanoscience & nanotechnology Automation Computer Science Applications Human-Computer Interaction Task (computing) Recurrent neural network Control and Systems Engineering Test set Robot Computer Vision and Pattern Recognition Artificial intelligence 0210 nano-technology business Fiducial marker 0503 education Robotics (cs.RO) |
| Popis: | Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as cable stretching and hysteresis. We propose a novel approach to efficiently calibrate such robots by placing a 3D printed fiducial coordinate frames on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling and history-dependent effects between joints, we analyze data from sampled trajectories and consider 13 approaches to modeling. These models include linear regression and LSTM recurrent neural networks, each with varying temporal window length to provide compensatory feedback. With the proposed method, data collection of 1800 samples takes 31 minutes and model training takes under 1 minute. Results on a test set of reference trajectories suggest that the trained model can reduce the mean tracking error of the physical robot from 2.96 mm to 0.65 mm. Results on the execution of open-loop trajectories of the FLS peg transfer surgeon training task suggest that the best model increases success rate from 39.4 % to 96.7 %, producing performance comparable to that of an expert surgical resident. Supplementary materials, including code and 3D-printable models, are available at https://sites.google.com/berkeley.edu/surgical-calibration 8 pages, 11 figures, 3 tables |
| Databáze: | OpenAIRE |
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