Semi-Autonomous Interventional Manipulation using Pneumatically Attachable Flexible Rails

Ettorre, C D; Stilli, Agostino; Dwyer, George; Neves, Joana B.; Tran, Maxine; Stoyanov, Danail

doi:10.1109/iros40897.2019.8967789

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…The PAF rail, shown in Fig. 1, can be of great help for the surgeon when performing the aforementioned procedures [6,7]. The PAF rail uses a series of vacuum-actuated suction cups to pair with the targeted organ/tissue providing a flexible interface that enables safe manipulation.…”

Section: Automation Of Intra-operative Organ Manipulationmentioning

confidence: 99%

Learning intraoperative organ manipulation with context-based reinforcement learning

et al. 2022

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Purpose Automation of sub-tasks during robotic surgery is challenging due to the high variability of the surgical scenes intra- and inter-patients. For example, the pick and place task can be executed different times during the same operation and for distinct purposes. Hence, designing automation solutions that can generalise a skill over different contexts becomes hard. All the experiments are conducted using the Pneumatic Attachable Flexible (PAF) rail, a novel surgical tool designed for robotic-assisted intraoperative organ manipulation. Methods We build upon previous open-source surgical Reinforcement Learning (RL) training environment to develop a new RL framework for manipulation skills, rlman. In rlman, contextual RL agents are trained to solve different aspects of the pick and place task using the PAF rail system. rlman is implemented to support both low- and high-dimensional state information to solve surgical sub-tasks in a simulation environment. Results We use rlman to train state of the art RL agents to solve four different surgical sub-tasks involving manipulation skills using the PAF rail. We compare the results with state-of-the-art benchmarks found in the literature. We evaluate the ability of the agent to be able to generalise over different aspects of the targeted surgical environment. Conclusion We have shown that the rlman framework can support the training of different RL algorithms for solving surgical sub-task, analysing the importance of context information for generalisation capabilities. We are aiming to deploy the trained policy on the real da Vinci using the dVRK and show that the generalisation of the trained policy can be transferred to the real world.

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Section: Automation Of Intra-operative Organ Manipulationmentioning

confidence: 99%

Learning intraoperative organ manipulation with context-based reinforcement learning

et al. 2022

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“…For the suturing task, including the works related to knot tying and needle insertion, we reported the following: [16], [70], [85], [88], [89], [161], [178], [195]- [197], [203], [243], and [246]. The pick, transfer, and place task was mainly characterized by experiments relying on pegs and rings from the fundamentals of the laparoscopic surgery training paradigm [54], [69], [91], [96], [102], [151]- [153], [163], [264] or new surgical tools [177]. A lot of the remaining works focused on tissue interaction.…”

Section: Instrument Controlmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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“…Stable track-guided US scanning is achieved by connecting the drop-in US probe to the perimeter of the rail. In Ettorre et al. (2019) the authors have investigated the autonomous deployment of the PAF rail on the surface of the organ and its use in intraoperative organ manipulation.…”

Section: Introductionmentioning

confidence: 99%

Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery

et al. 2023

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In robotic-assisted partial nephrectomy, surgeons remove a part of a kidney often due to the presence of a mass. A drop-in ultrasound probe paired to a surgical robot is deployed to execute multiple swipes over the kidney surface to localise the mass and define the margins of resection. This sub-task is challenging and must be performed by a highly-skilled surgeon. Automating this sub-task may reduce cognitive load for the surgeon and improve patient outcomes. The eventual goal of this work is to autonomously move the ultrasound probe on the surface of the kidney taking advantage of the use of the Pneumatically Attachable Flexible (PAF) rail system, a soft robotic device used for organ scanning and repositioning. First, we integrate a shape-sensing optical fibre into the PAF rail system to evaluate the curvature of target organs in robotic-assisted laparoscopic surgery. Then, we investigate the impact of the PAF rail’s material stiffness on the curvature sensing accuracy, considering that soft targets are present in the surgical field. We found overall curvature sensing accuracy to be between 1.44% and 7.27% over the range of curvatures present in adult kidneys. Finally, we use shape sensing to plan the trajectory of the da Vinci surgical robot paired with a drop-in ultrasound probe and autonomously generate an Ultrasound scan of a kidney phantom.

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Semi-Autonomous Interventional Manipulation using Pneumatically Attachable Flexible Rails

Cited by 8 publications

References 25 publications

Learning intraoperative organ manipulation with context-based reinforcement learning

Learning intraoperative organ manipulation with context-based reinforcement learning

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Organ curvature sensing using pneumatically attachable flexible rails in robotic-assisted laparoscopic surgery

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