Vision-Based Calibration of Dual RCM-Based Robot Arms in Human-Robot Collaborative Minimally Invasive Surgery
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AbstractThis letter reports the development of a vision-based calibration method for dual remote center-of-motion (RCM) based robot arms in a human-robot collaborative minimally invasive surgery (MIS) scenario. The method does not require any external tracking sensors and directly uses images captured by the endoscopic camera and the robot encoder readings as calibration data, which leads to a minimal and practical system in the operating room. By taking advantage of the motion constraints imposed by the RCM-based kinematics of the robotic surgical tools and cameras, we can find unique relationships between the endoscope and the surgical tool using camera perspective projection geometry without the geometric information of the tool. A customized vision-based centerline detection algorithm is also proposed, which provides robust estimation of centerline positions for a variety of settings. We validate the method through simulations and an experimental study in a simulated MIS scenario, in which the first generation da Vinci surgical system controlled by the open source da Vinci research kit electronics and cisst/SAW software environment is used.
Acceptance Date09/08/2017
All Author(s) ListZerui Wang, Ziwei Liu, Qianli Ma, Alexis Cheng, Yun-hui Liu, Sungmin Kim, Anton Deguet, Austin Reiter, Peter Kazanzides, Russell H. Taylor
Journal nameIEEE Robotics and Automation Letters
Year2018
Month4
Volume Number3
Issue Number2
PublisherIEEE
Pages672 - 679
ISSN2377-3766
LanguagesEnglish-United States
KeywordsCalibration and identification, computer vision for medical robotics, surgical robotics: laparoscopy.

Last updated on 2021-25-09 at 23:10