Navigation Method for Teleoperated Single-Port Access Surgery With Soft Tissue Interaction Detection

Pulgar, C. J. Pérez del; García-Morales, I.; Rivas-Blanco, Irene; Muñoz, V.F.

doi:10.1109/jsyst.2016.2570118

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…The surgical robotic platform was composed of three arms; two of them that make up the CISOBOT platform, in charge of the teleoperation of the surgical instruments, and a UR3 robot, from Universal Robots, with a commercial 2D endoscope attached at its end-effector. The CISOBOT is an experimental platform developed at the University of Malaga, which is composed of two customized six degrees-of-freedom manipulators with a laparoscopic grasper tool attached at their end-effectors [29]. These robots are teleoperated using the master console that is shown in Figure 3, which is composed of two haptic devices and a monitor that displays the image of the camera.…”

Section: Resultsmentioning

confidence: 99%

Transferring Know-How for an Autonomous Camera Robotic Assistant

et al. 2019

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Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms.

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Section: Resultsmentioning

confidence: 99%

Transferring Know-How for an Autonomous Camera Robotic Assistant

et al. 2019

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“…The new "interaction-force" based method proposed in this paper provides a viable way to encode and reconstruct contact tasks in robotic assembly to guide human operators whenever either visual information is limited or when positional or time-based indexed guidance data are not available or difficult to achieve. The LfD-based framework proposed by us earlier [18] for haptic shared control is extended by an LfD-based gesture recognition module, which allows application in real-time, due to its linear increase of computational complexity with the number of Gaussian Models used to encode any gesture. We consider the proposed approach to be extendable to other complex manipulation tasks involving contact operations, such as operation on knobs, doors, hinges, and levers, and, therefore, we assume that the method can significantly improve remotely operated robotic manipulation in a variety of contexts.…”

Section: Discussionmentioning

confidence: 99%

“…Interaction of the slave robot with the environment generates forces − → f and torques − → τ that are obtained from the robot sensors. They are used by the gesture recognizer to output the gesture ρ i that is being carried, and it is used to provide the haptic guidance reference to the operator using the method described in the previous contribution [18]. Hereafter, details of the gesture recognizer system are described.…”

Section: Lfd For Gesture Recognitionmentioning

confidence: 99%

“…In this sense, gesture recognition has been widely studied with different methods and applications [16]. For years, gesture recognition has usually been addressed using a continuous or discrete hidden Markov model (HMM) [17,18]. An HMM can encode previously trained gestures as a sequence of states with probabilistic relationships between states and measurements.…”

Section: Introductionmentioning

confidence: 99%

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Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

et al. 2019

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Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time. During the last years, the use of learning from demonstration (LfD) has been proposed to perform interactive guidance based on simple tasks that are usually composed of a single stage. However, it would be desirable to improve this approach to solve complex tasks composed of several stages or gestures. This paper extends the LfD approach for object telemanipulation where the task to be solved is divided into a set of gestures that need to be detected. Thus, each gesture is previously trained and encoded within a Gaussian mixture model using LfD, and stored in a gesture library. During telemanipulation, depending on the sensory information, the gesture that is being carried out is recognized using the same LfD trained model for haptic guidance. The method was experimentally verified in a teleoperated peg-in-hole insertion task. A KUKA LWR4+ lightweight robot was remotely controlled with a Sigma.7 haptic device with LfD-based shared control. Finally, a comparison was carried out to evaluate the performance of Gaussian mixture models with a well-established gesture recognition method, continuous hidden Markov models, for the same task. Results show that the Gaussian mixture models (GMM)-based method slightly improves the success rate, with lower training and recognition processing times.

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“…El empleo de estas plataformas aparece como una solución que podría apaliar las desventajas explicadas anteriormente, se podría disponer de un sistema capaz de detectar y esquivar la colisión de instrumentos, control del movimiento de los instrumentos con el fin de facilitar la ejecución de estos, etc. En este sentido, la Universidad de Málaga se encuentra desarrollando la plataforma experimental de teleoperación CISOBOT [2] [7]. Este artículo contribuye al desarrollo de esta plataforma con el diseño mecatrónico de un instru- mento motorizado que permite mover la punta y el control del mismo mediante una consola de teleoperación que emplea dispositivos hápticos.…”

Section: Introductionunclassified

Teleoperación de instrumentos quirúrgicos articulados

Delgado¹,

Mancebo²,

Terol³

et al. 2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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Todos los derechos reservados. De conformidad con lo dispuesto en la legislación vigente, podrán ser cas�gados con penas de multa y privación de libertad quienes reproduzcan o plagien, en todo o en parte, una obra literaria, ar�s�ca o cien�fica, fijada en cualquier �po y soporte, sin la precep�va autorización. JA2017 Prefacio Prefacio Las Jornadas de Automática se celebran desde hace 40 años en una universidad nacional facilitando el encuentro entre expertos en estaárea en un foro que permite la puesta en común de las nuevas ideas y proyectos en desarrollo. Al mismo tiempo, propician la siempre necesaria colaboración entre investigadores delámbito de la Ingeniería de Control y Automática, así como de campos afines, a la hora de abordar complejos proyectos de investigación multidisciplinares. En esta ocasión, las Jornadas estarán organizadas por la Universidad de Oviedo y se han celebrado del 6 al 8 de septiembre de

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Navigation Method for Teleoperated Single-Port Access Surgery With Soft Tissue Interaction Detection

Cited by 6 publications

References 28 publications

Transferring Know-How for an Autonomous Camera Robotic Assistant

Transferring Know-How for an Autonomous Camera Robotic Assistant

Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

Teleoperación de instrumentos quirúrgicos articulados

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