The da Vinci Surgical System

DiMaio, Simon P.; Hanuschik, Mike; Kreaden, Usha

doi:10.1007/978-1-4419-1126-1_9

Cited by 85 publications

(64 citation statements)

References 8 publications

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“…The object is fixed in the working area of a standard dual arm da Vinci system [1]. As the image stream flows at 30 fps, the RGB data, depth data, and force data are synchronized to be within 10 ms.…”

Section: Dataset Collectionmentioning

confidence: 99%

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Gao

Liu

Peven

et al. 2018

Lecture Notes in Computer Science

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Robotic surgery has been proven to offer clear advantages during surgical procedures, however, one of the major limitations is obtaining haptic feedback. Since it is often challenging to devise a hardware solution with accurate force feedback, we propose the use of "visual cues" to infer forces from tissue deformation. Endoscopic video is a passive sensor that is freely available, in the sense that any minimally-invasive procedure already utilizes it. To this end, we employ deep learning to infer forces from video as an attractive low-cost and accurate alternative to typically complex and expensive hardware solutions. First, we demonstrate our approach in a phantom setting using the da Vinci Surgical System affixed with an OptoForce sensor. Second, we then validate our method on an ex vivo liver organ. Our method results in a mean absolute error of 0.814 N in the ex vivo study, suggesting that it may be a promising alternative to hardware based surgical force feedback in endoscopic procedures.

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Section: Dataset Collectionmentioning

confidence: 99%

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Gao

Liu

Peven

et al. 2018

Lecture Notes in Computer Science

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“…The master is defined as the surgical console and the slave serves as the surgical robot itself interacting with the patient's tissue through the surgical tools. [6][7][8].…”

Section: A U T O N O M O U S O P E R a T I O N I N S U R G I C A L R mentioning

confidence: 99%

Autonomous Operation in Surgical Robotics

Rosén¹,

Ma²

2015

Mechanical Engineering

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The article focuses on developing an algorithm for automation based on stereo computer vision and dynamic registration in a surgical robotic context. The performance of the algorithm was further tested experimentally utilizing the block transfer task which corresponds to tissue manipulation as designed by Fundamentals of Laparoscopic Surgery (FLS). The surgical robotics field as a whole progresses towards the reduction of invasiveness limiting the trauma at the periphery of the surgical site and increase of semi-autonomous operation while positioning the surgeon as a decision maker rather than as an operator. The autonomous FLS task is implemented successfully and tested experimentally with the Raven II surgical robot system. The data indicate that the autonomous operational mode has better overall performance and limited tool-environment interaction compared with the human teleoperation mode. Surgeon’s intention may also be extracted from a database that may lead to seamless switching between the human operator and the autonomous system and in that sense, it may allow the autonomous algorithm to cope with more complex surgical environments.

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“…With the implementation of remote control technology, it is possible to accomplish the tasks that cannot be autonomously performed. To date, several remote control robot applications exist and include remote surgery, [5][6][7] remote astronautic robot, 8,9 telemedicine, and others. 4,10,11 Previously, most remote control applications in consumer electronics were used to operate devices, such as a television set, DVD player, or other home appliances, 12 from a short distance.…”

Section: Introductionmentioning

confidence: 99%

Improvement in human error by target predication in transmission control protocol/Internet protocol–based remote control system

Tsai

Liaw

2017

Advances in Mechanical Engineering

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In a robot teleoperation system, humans operate and respond by relying on the content displayed on the video screen. However, unavoidable delays, such as network transmission, cause asynchronous communication between humans and robot and further result in human operation errors such as redundant or duplicate operations or even wrong commands. Users of consumer electronics in daily life are different from users in large-scale industry as they may not be experts or even trained to use the robots leading to new challenges in developing home-used robots. In this study, a module-based remote control system is designed to illustrate the possible human operation errors caused by transmission delay of transmission control protocol/Internet protocol-based network and to provide a user-friendly framework for simulating solutions to improve human errors. This is followed by proposing two target prediction methods, namely the target area predictor and stop position predictor, to decrease the human errors. The experimental results show that target area predictor and stop position predictor effectively improve the performance by reducing task completion time and object trajectory errors by 60% and 36%, respectively.

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The da Vinci Surgical System

Cited by 85 publications

References 8 publications

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Learning to See Forces: Surgical Force Prediction with RGB-Point Cloud Temporal Convolutional Networks

Autonomous Operation in Surgical Robotics

Improvement in human error by target predication in transmission control protocol/Internet protocol–based remote control system

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