Attanasio, A, Marahrens, N orcid.org/0000-0002-5982-4990, Scaglioni, B orcid.org/0000-0003-4891-8411 et al. (1 more author) (2021) An Open Source Motion Planning Framework for Autonomous Minimally Invasive Surgical Robots. In: 2021 IEEE International Conference on Autonomous Systems (ICAS). 2021 IEEE International Conference on Autonomous Systems (ICAS), 11-13 Aug 2021, Montreal, QC, Canada. IEEE ISBN 978-1-7281-7290-3
Abstract
Planning and execution of autonomous tasks in minimally invasive surgical robotic are significantly more complex with respect to generic manipulators. Narrow abdominal cavities and limited entry points restrain the use of external vision systems and specialized kinematics prevent the straightforward use of standard planning algorithms. In this work, we present a novel implementation of a motion planning framework for minimally invasive surgical robots, composed of two subsystems: An arm-camera registration method only requiring the endoscopic camera and a graspable device, compatible with a 12mm trocar port, and a specialized trajectory planning algorithm, designed to generate smooth, non straight trajectories. The approach is tested on a DaVinci Research Kit obtaining an accuracy of 2.71±0.89 cm in the arm-camera registration and of 1.30±0.39 cm during trajectory execution. The code is organised into STORM Motion Library (STOR-MoLib), an open source library, publicly available for the research community.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society wm150122 EPSRC (Engineering and Physical Sciences Research Council) EP/R045291/1 Intuitive Surgical Inc Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Nov 2021 13:27 |
Last Modified: | 19 Nov 2021 03:15 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icas49788.2021.9551134 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180011 |