Robotics & Machine Learning Daily News2024,Issue(Jun.18) :115-115.

Findings from University of Augsburg Has Provided New Data on Robotics (Selectin g Feasible Trajectories for Robot-based X-ray Tomography By Varying Focus-detect or-distance In Space Restricted Environments)

奥格斯堡大学的发现提供了机器人学的新数据(选择G可行轨迹,用于在空间受限环境中通过改变焦点探测或距离进行基于机器人的X射线断层成像)

Robotics & Machine Learning Daily News2024,Issue(Jun.18) :115-115.

Findings from University of Augsburg Has Provided New Data on Robotics (Selectin g Feasible Trajectories for Robot-based X-ray Tomography By Varying Focus-detect or-distance In Space Restricted Environments)

奥格斯堡大学的发现提供了机器人学的新数据(选择G可行轨迹,用于在空间受限环境中通过改变焦点探测或距离进行基于机器人的X射线断层成像)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的新研究是一份报告的主旨。根据来自德国奥格斯堡的新闻报道,BY NewsRx通讯记者Research称:“计算机断层摄影术已经发展成为汽车工业无损检测的一种重要工具。基于机器人的计算机断层摄影术的应用使部件的高分辨率CT成像能够超过常规系统的尺寸。”这项研究的财政支持来自Ing博士。高压F .保时捷公司我们的新闻编辑引用了奥格斯堡大学的一篇研究文章:“然而,大型部件,如车身,往往表现出极限的元素。由于某些角度方向的不可逆性,使用具有恒定焦距-探测器-距离的常规轨迹会导致图像数据的各向异性。”本文介绍了两种方法,即通过对不同焦距投影的积分,选择合适的采集点集,使X射线硬件的可变距离能够在碰撞结构周围导航,从而方便了对无角方向的扫描。通过保持焦点-物体距离和物体-探测器-距离之间的恒定比率来保持视场,同时谨慎地延长焦点-探测器-距离。第二种方法代表了一种更直接的方法,通过绕过这些碰撞元件周围的X射线源,可以扫描以前在常规圆形通道上无法到达的angul ar扇区。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting originating from Augsburg, Germany, b y NewsRx correspondents, research stated, "Computed tomography has evolved as an essential tool for non-destructive testing within the automotive industry. The application of robot-based computed tomography enables high-resolution CT inspec tions of components exceeding the dimensions accommodated by conventional system s." Financial support for this research came from Dr. Ing. h.c. F. Porsche AG. Our news editors obtained a quote from the research from the University of Augsb urg, "However, large-scale components, e.g. vehicle bodies, often exhibit trajec tory-limiting elements. The utilization of conventional trajectories with consta nt Focus-Detector-Distances can lead to anisotropy in image data due to the inac cessibility of some angular directions. In this work, we introduce two approache s that are able to select suitable acquisitions point sets in scans of challengi ng to access regions through the integration of projections with varying Focus-D etector-Distances. The variable distances of the X-ray hardware enable the capab ility to navigate around collision structures, thus facilitating the scanning of absent angular directions. The initial approach incorporates collision-free vie wpoints along a spherical trajectory, preserving the field of view by maintainin g a constant ratio between the Focus-Object-Distance and the Object-Detector-Dis tance, while discreetly extending the Focus-Detector-Distance. The second method ology represents a more straightforward approach, enabling the scanning of angul ar sectors that were previously inaccessible on the conventional circular trajec tory by circumventing the X-ray source around these collision elements."

Key words

Augsburg/Germany/Europe/Emerging Tech nologies/Machine Learning/Robot/Robotics/University of Augsburg

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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