Vision-Based Dynamic Virtual Fixtures for Tools Collision Avoidance in Robotic Surgery

Moccia, Rocco; Iacono, Cristina; Siciliano, Bruno; Ficuciello, Fanny

doi:10.1109/lra.2020.2969941

Cited by 37 publications

(22 citation statements)

References 18 publications

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“…Based on the scene segmentation of the MICCAI Challenge 2018 dataset, they generated a graph-based tissue-tool interaction dataset with new annotations. Moccia et al [105] propose to use instrument segmentation to develop shared control techniques based on virtual fixtures to avoid instruments collision during surgery.…”

Section: A Development Of Autonomous Surgical Systemsmentioning

confidence: 99%

A Review on Deep Learning in Minimally Invasive Surgery

et al. 2021

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In the last five years, deep learning has attracted great interest in computer-assisted systems for Minimally Invasive Surgery. The straightforward accessibility to images in surgical interventions makes deep neural networks enormously powerful for solving classification problems in complex surgical scenarios. The objective of this work is to provide readers a survey on deep learning models applied to minimally invasive surgery, identifying the different architectures used depending on the application, the results achieved until now, and the publicly available surgical datasets that can be used for validating new studies. A total of 85 publications have been extracted from manual research from four databases (IEEE Xplorer, Springer Link, Science Direct, and ACM Digital Library). After analyzing all these studies, they have been classified into four applications: surgical image analysis, surgical task analysis, surgical skill assessment, and automation of surgical tasks. This work provides a technical description of these works and a comparison among them. Finally, promising research directions to advance in this field are identified.

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Section: A Development Of Autonomous Surgical Systemsmentioning

confidence: 99%

A Review on Deep Learning in Minimally Invasive Surgery

et al. 2021

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“…When working offline, it is useful to use collision detection. Due to the importance of the problem, work is also being carried out to automatically avoid collisions and retreat from problematic points [ 67 , 68 , 69 ].…”

Section: Related Workmentioning

confidence: 99%

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

Kaczmarek

Lotys²,

Borys

et al. 2021

Sensors

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The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.

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“…Obstacle avoidance is one of the most critical requirements to be met in many robotics scenarios. In this matter, researchers have been using haptic feedback to warn the operator about instantaneous collisions [9], [10]. Several human-collaborative schemes are compared in [11] where it is shown that haptic feedback is one of the main aspects.…”

Section: A Related Workmentioning

confidence: 99%

“…As an example, we report in Table I the values of the dynamic parameters appearing in (9) corresponding to the MikroKopter 2 quadrotor platform. Note that we perform model scaling as MPC uses normalized input/output variables.…”

Section: Predicted Healthiness Indexmentioning

confidence: 99%

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

Carlos

Franchi

Oriolo

2021

IEEE Robot. Autom. Lett.

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This paper presents a novel algorithm for blending human inputs and automatic controller commands, guaranteeing safety in mixed-initiative interactions between humans and quadrotors. The algorithm is based on nonlinear model predictive control (NMPC) and involves using the state solution to assess whether safety-and/or task-related rules are met to mix control authority. The mixing is attained through the convex combination of human and actual robot costs and is driven by a continuous function that measures the rules' violation. To achieve real-time feasibility, we rely on an efficient real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme to cast the mixed-initiative controller. We demonstrate the effectiveness of our algorithm through numerical simulations, where a second autonomous algorithm is used to emulate the behavior of pilots with different skill levels. Simulations show that our scheme provides suitable assistance to pilots, especially novices, in a workspace with obstacles while underpinning computational efficiency.

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Vision-Based Dynamic Virtual Fixtures for Tools Collision Avoidance in Robotic Surgery

Cited by 37 publications

References 18 publications

A Review on Deep Learning in Minimally Invasive Surgery

A Review on Deep Learning in Minimally Invasive Surgery

Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

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