Implementación de un sistema de visión computacional para el robot Scorbot en una célula flexible de mecanizado didáctica

77 p.El objetivo de esta memoria es la implementación de un sistema de visión computacional y control de un brazo robótico Scorbot en una célula flexible de mecanizado del Laboratorio de Manufactura Integrada por Computador de la Escuela de Ingeniería Mecánica de la Universidad de Talca. El movimiento que posee el brazo al momento de coger los objetos se encuentra limitado a una única posición, lugar en donde éstos deben ser ubicados para poder ser tomados. Esta limitante ha motivado la implementación de un sistema de visión computacional para dar mayor autonomía a la manera en que el robot recoge los objetos desde su área de trabajo. Las bases del sistema de visión computacional se centran en el procesamiento de imágenes, y más aún, en la detección de los bordes del objeto. Para lograr esto último, se han llevado a cabo procesos de binarización, que eliminan el ruido y la información innecesaria de las imágenes; resaltado de bordes, para obtener una imagen sólo con los bordes que serían detectados osteriormente, mediante un algoritmo llamado transformada de Hough. Con este último, se puede determinar, mediante parámetros, la posición de cada uno de los bordes del objeto y con este resultado se puede obtener su centroide, lugar que será en donde el robot debería posicionarse para tomar el objeto, considerando la orientación que éste posee. Luego, mediante programación del puerto serial, se envían los datos de posición al controlador del robot para moverlo a la posición deseada. Todo este proceso permite crear un sistema de visión computacional orientado a resolver este problema específico, utilizando una cámara Web convencional, un computador director y el brazo robótico propiamente tal. Para este caso en particular, la utilización de estos algoritmos de procesamiento de imagen permiten calcular correctamente la posición de los objetos, y la interconexión de estas tres componentes crean canales de comunicación para que éstas interactúen./ ABSTRACT: The objective of this report is the implementation of a vision system and computer control of a robotic arm Scorbot in a flexible machining cell in the Laboratory of Computer Integrated Manufacturing of the School of Mechanical Engineering at the University of Talca. The movement which owns the arm when catch objects is limited to a single position, a place where they must be located to be taken. This limitation has led to the implementation of a computer vision system to give greater autonomy to the manner in which the robot collects objects from your work area. The system is based on computer vision and image processing, with the specific goal of detecting the edges of the object to be grasped by the robot manipulator. The image is smoothed, binarized and filtered to show only object edges. Then the Hough transform is applied, which gives the parameters of the important straight lines in the image. The object centroid is calculated and used as the goal for positioning the robot manipulator. The image processing computer then communicates over the serial port with the robot controller to send the commands to move the robot arm to the desired position. The system components include a conventionalWeb camera, a guidance computer and the robot arm itself. The results of this experience demonstrate that the system is able to calculate correctly the position of the objects, communicate with and control the robot arm

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries