Optimal needle grasp selection for automatic execution of suturing tasks in robotic minimally invasive surgery

Liu, Taoming; Çavuşoğlu, M. Cenk

doi:10.1109/icra.2015.7139594

Cited by 17 publications

(5 citation statements)

References 18 publications

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“…Six common surgical tasks appear to be widely investigated for automation. 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].…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Three parameters are considered in surgical needle grasping: Manipulation, dexterity and torque metrics. Each of these parameters is important for a successful needle grasping ( 8 ).…”

Section: Current Developmentsmentioning

confidence: 99%

Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!” What is autonomous surgery and what are the latest developments?

Gültekin

Karabük

Köse

2021

J Turkish German Gynecol Assoc

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As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as “What is autonomic surgery?” and in which areas studies are progressing.

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“…Fontanelli et al cite that 74% of suture throws from the in-vivo JIGSAW dataset [10] required regrasping which took on average 7.4s to complete [11]. Previous work has also shown that proper grasping can enhance the effectiveness of suture throws [8], [12]. In fact, many planning techniques for suture needle throwing have even used task-specific gripper mechanisms to ensure proper grasping of a needle [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning

Chiu

Richter

Funk

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Regrasping a suture needle is an important process in suturing, and previous study has shown that it takes on average 7.4s before the needle is thrown again. To bring efficiency into suturing, prior work either designs a taskspecific mechanism or guides the gripper toward some specific pick-up point for proper grasping of a needle. Yet, these methods are usually not deployable when the working space is changed. These prior efforts highlight the need for more efficient regrasping and more generalizability of a proposed method. Therefore, in this work, we present rapid trajectory generation for bimanual needle regrasping via reinforcement learning (RL). Demonstrations from a sampling-based motion planning algorithm is incorporated to speed up the learning. In addition, we propose the ego-centric state and action spaces for this bimanual planning problem, where the reference frames are on the end-effectors instead of some fixed frame. Thus, the learned policy can be directly applied to any robot configuration and even to different robot arms. Our experiments in simulation show that the success rate of a single pass is 97%, and the planning time is 0.0212s on average, which outperforms other widely used motion planning algorithms. For the real-world experiments, the success rate is 73.3% if the needle pose is reconstructed from an RGB image, with a planning time of 0.0846s and a run time of 5.1454s. If the needle pose is known beforehand, the success rate becomes 90.5%, with a planning time of 0.0807s and a run time of 2.8801s.

show abstract

Optimal needle grasp selection for automatic execution of suturing tasks in robotic minimally invasive surgery

Cited by 17 publications

References 18 publications

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

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

Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!” What is autonomous surgery and what are the latest developments?

Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning

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