Autonomous suturing via surgical robot: An algorithm for optimal selection of needle diameter, shape, and path

Pedram, Sahba Aghajani; Ferguson, Peter Walker; Ma, Ji; Dutson, Erik; Rosén, Jacob

doi:10.1109/icra.2017.7989278

Cited by 65 publications

(31 citation statements)

References 23 publications

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“…If the tissue points are close to their desired positions, it is not necessary to use all inputs in the input horizon. Thus, the optimal number of inputs in the input horizon is also found in equations (1). As a result, the output from the MPC formulated in equations (1) is a set {u * t , · · · , u * t+h * }.…”

Section: Methodsmentioning

confidence: 99%

Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

Shin

Ferguson

Pedram

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated that the learning from demonstration algorithm can boost the learning policy by initializing the predicted dynamics with given demonstrations. Furthermore, the learning from demonstration algorithm is implemented on a Raven IV surgical robotic system and successfully demonstrated feasibility of the proposed algorithm using an experimental approach. This study is part of a profound vision in which the role of a surgeon will be redefined as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject17.html

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Section: Methodsmentioning

confidence: 99%

Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

Shin

Ferguson

Pedram

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Much of this work has focused on issues related to control, sensing, and software. A challenging frontier for surgical robotic control software, drawing increasing study, is augmenting teleoperation with autonomous functions [27][28][29][30][31][32]. Such functions may trade control authority back and forth between computer and surgeon, or may share control of different degrees of freedom simultaneously with the surgeon [33].…”

Section: Research Using Ravenmentioning

confidence: 99%

“…In a major contrast with the broader robotics community, there is only limited research on autonomy in robotic surgeries [27-29, 40, 43, 53, 70, 75]. Among these works, many focus on autonomizing surgical tasks, such as needle insertion [27] and suturing [28]. There are also some works on motion planning, either motion pattern planning [40,53], or the planning of the motion trajectories [43,70].…”

Section: Autonomous Robotic Surgeriesmentioning

confidence: 99%

The Raven Open Surgical Robotic Platforms: A Review and Prospect

2019

APH

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The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

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“…Based on clinical suturing guidelines and using kinematic model of needle-tissue interaction with a surgeon that specified entry/exit points, Pedram et al developed an algorithm for optimal selection of a needle diameter, shape, and constant curvature path 14 . The proposed algorithm was tested experimentally with the Raven II surgical system on a tissue phantom to guarantee minimal tissue trauma and compliance with suturing requirements.…”

Section: Related Workmentioning

confidence: 99%

Automated open wound suturing: detection and planning algorithm

Sagitov¹,

Tsoy²,

Li³

et al. 2018

JRNAL

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Today one of the key disadvantages of robotic surgery is a lack of a haptic feedback, while in traditional surgery a surgeon uses human haptic senses in all tasks. Providing robots with a haptic feedback have a high potential of reducing time that is spent by a surgeon on suturing subtasks and helping to reduce a surgeon fatigue. Decreasing manual input of a surgeon enables remote surgery even under long communication links with significant latency. In this paper we present a framework of wound detection and suture planning. We plan to implement and test our algorithms using KUKA iiwa manipulator.

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Autonomous suturing via surgical robot: An algorithm for optimal selection of needle diameter, shape, and path

Cited by 65 publications

References 23 publications

Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control

The Raven Open Surgical Robotic Platforms: A Review and Prospect

Automated open wound suturing: detection and planning algorithm

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