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Artificial Vision in Embedded System for Rigid Parts Recognition

机译:嵌入式系统中用于硬零件识别的人工视觉

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Since the first robot manipulator for industrial applications was installed for General Motors, the planning and execution of its movements has been an important part in the development of robotic systems involving researchers from different specialties. The planning and guidance of the movement in the robots has been increasingly complex due to the wide variety of applications in which they are used, from repetitive tasks in the traditional assembly lines to the assistance in the movements of very precise surgical operations which require real-time movement guidance, this has established an area of research and technological development known as “Robotics hardware and software driving”. The article shows the implementation of the methodology called “Boundary Object Function BOF” [Peña 2005], algorithm for recognition and location of rigid forms, in an embedded electronic device of the RaspBerry Pi type 3. In the method used, the electronic system acquires and condition the image, to be converted to a binary image used by the BOF algorithm. Experimental results within a manufacturing cell were performed with the implementation of the method. The result of the integration of recognition algorithms and location of rigid manufacturing parts in embedded electronic systems, shows the possibility of using them in manufacturing applications with processing high speed requirements and concurrent processes, in this way, a robot learns online and identify objects that are familiar to the performed tasks. The technological proposal presented for the invariant recognition of objects based on the BOF algorithm implemented in an electronic embedded system calculates the contour of rigid pieces very quickly.
机译:自从为通用汽车安装了第一台工业应用的机器人操纵器以来,其动作的计划和执行一直是涉及不同领域的研究人员的机器人系统开发的重要组成部分。由于机器人的用途广泛,从传统装配生产线的重复性任务到非常精确的外科手术运动的辅助,机器人在运动中的计划和指导变得越来越复杂,这需要在时间运动的指导下,这建立了一个研究和技术开发领域,即“机器人硬件和软件驱动”。本文介绍了在RaspBerry Pi类型3的嵌入式电子设备中实现称为“边界对象函数BOF”的方法(Peña2005)的实现,该算法用于识别和定位刚性形式。在使用的方法中,电子系统获得并调节图像,以将其转换为BOF算法使用的二进制图像。该方法的实施在制造单元内进行了实验结果。集成识别算法和嵌入式电子系统中刚性制造零件的位置的结果表明,可以在具有高速处理要求和并发处理能力的制造应用中使用它们,这样,机器人就可以在线学习并识别物体。熟悉所执行的任务。提出的基于在电子嵌入式系统中实现的BOF算法用于物体的不变识别的技术建议可以非常快速地计算出刚性零件的轮廓。

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