機器人輔助計算機斷層掃描系統
弗勞恩霍夫開發中心X射線技術EZRT開發了“RoboCT”，一種機器人輔助計算機斷層掃描（CT）系統，可在汽車生產的早期開發階段分析車輛質量 - 無需拆卸車輛 - 從而縮短了開發周期。作為與汽車制造商BMW合作的一部分，該系統現已安裝在生產環境中。
該機器人能夠到達具有復雜形狀的物體（例如車身）或特別大的工作空間中的測試位置。Fraunhofer EZRT的研究人員開發了RoboCT技術，Fraunhofer EZRT是弗勞恩霍夫集成電路研究所的一個部門。初專門針對航空航天測試，例如檢查整個機翼的缺陷，大約13年的研究進入了這項技術，由公共資助和獨立的研究項目提供資金。通過與慕尼黑研究與創新中心（FIZ）寶馬集團工程師的密切合作，CT系統直接安裝在開發和生產之間的接口上，并于2018年7月投入運行。
在他們的設置中，操縱成像組件的四個協作機器人（例如X射線源和探測器）在汽車周圍行進，使RoboCT能夠到達車輛上的所有位置。通過這種方式，系統可以產生三維CT圖像，其顯示與人類頭發一樣小的細節。利用這項技術，可以極其精確地分析物體，而不會損壞物體。到目前為止，執行此級別的分析需要在單獨的CT系統中拆卸甚至切割和分析相關組件。較短的開發周期意味著用戶可以更快地將產品從創意階段推向市場。
算法糾正機器人的不準確性
工業中常用的X射線CT系統能夠掃描直徑約30厘米的物體，以獲取所有結構的3D信息，無論這些信息是表面的還是隱藏在物體的內部。這些CT圖像可以在計算機上虛擬切片成任何所需的截面圖并進行分析。需要超精密硬件組件才能實現有時小于1微米的分辨率。大型工業機器人 - 這里的范圍為3米或更大 - 讓用戶可以在更大的物體和形狀復雜的物體上到達感興趣的區域（ROI）。這里涉及的特殊挑戰是使用算法直接從記錄的測量數據校正機器人的幾何不準確性。
這種尺寸的精確的工業機器人在整個工作區域內的精度僅為?至?毫米 - 但根據應用情況，CT需要至少1/20毫米。解決這個問題是在當今生產環境中使用該技術的關鍵。
下一步：認知傳感器系統
通過這些新發展實現的當前形式的基于機器人的CT只是一個更大想法的開始：長期目標不是簡單地隨機或批量地測量材料數據，而是僅獲取相關數據。至于哪些數據是相關的，認知傳感器系統本身將確定這一點?？蛻魧@得類似于高度靈活的黑匣子的東西。他們不需要以任何方式處理它們，并且他們不需要在非破壞性測試中擁有任何專業知識。例如，該盒子將包括可以訪問各種自適應傳感器系統的機器人，然后，從廣泛的意義上說，它們自己決定使用哪種模式以及如何使用它們。然后機器人激活X射線系統，空氣超聲系統或熱成像系統來完成特定的，精確定義的任務，而不僅僅是測試任何東西。通過使用人工智能，RoboCT將通過充當黑匣子來幫助用戶完成各種任務，根據手頭的工作，在可訪問性和獲取方面推薦佳參數。
原文如下：
原文如下：
The Fraunhofer Development Center X-ray Technology EZRT has developed “RoboCT,” a robot-assisted computed tomography (CT) system that analyzes vehicle quality in the early development phase of automotive production – without disassembling the vehicle – and thus shortens development cycles. As part of a collaboration with automaker BMW, this system has now been installed in the production environment.
Fraunhofer EZRT puts first robot-based CT system into operation at automaker BMW
This robot is able to reach test positions on objects with complex shapes, such as a car body, or in a particularly large workspace. Researchers at Fraunhofer EZRT, a division of the Fraunhofer Institute for Integrated Circuits IIS, developed the RoboCT technology. Originally aimed especially at aerospace testing, for example to inspect entire wings for defects, some 13 years of research went into this technology, financed both by publicly funded and independent research projects.
In close cooperation with engineers from the BMW Group at the Research & Innovation Centre (FIZ) in Munich, the CT system was installed directly at the interface between development and production, and was put into operation in July 2018.
In their setup, four cooperating robots that manipulate the imaging components, such as the X-ray source and detector, travel around the car, enabling RoboCT to reach all positions on the vehicle. In this way, the system can produce three-dimensional CT images showing details as small as a human hair. With this technology, objects can be analyzed in detail with extreme precision and without damaging them. Until now, performing this level of analysis required the relevant components to be disassembled or even cut out and analyzed in a separate CT system. The shorter development cycles mean that users can take a product from the idea stage to market launch much faster.
Algorithms correct robots’ inaccuracies
X-ray CT systems commonly used in industry are capable of scanning objects of about 30 centimeters in diameter to acquire 3D information on all their structures, whether these are superficial or hidden in the object’s interior. These CT images can be virtually sliced into any desired sectional views on a computer and analyzed. Ultraprecise hardware components are needed in order to achieve resolutions of sometimes less than one micrometer. Large industrial robots – here with ranges of three meters or more – let users reach regions of interest (ROI) on much larger objects and objects with complex shapes. The particular challenge involved here is the use of algorithms to correct the robots’ geometric inaccuracies directly from the recorded measurement data.
The most precise industrial robots of this size achieve accuracies of just ? to ? millimeter over their entire working area – but depending on the application, at least 1/20 millimeter is needed for CT. Solving this problem is the key to using this technology in today’s production environments.
The next step: cognitive sensor systems
The robot-based CT in its current form as realized by these latest developments is just the beginning of a larger idea: the long-term goal is not to simply measure material data at random or in bulk, but rather to acquire only the relevant data. As for which data is relevant, the cognitive sensor system itself will determine that. Customers will receive something akin to a highly flexible black box. They won’t need to deal with it in any way and they don’t need to have any sort of expertise whatsoever in non-destructive testing. The box will include, for instance, robots that have access to various self-adapting sensor systems and that then, in the broadest sense, decide for themselves which modalities to use and how to use them. The robot then activates an X-ray system, an air ultrasound system or a thermography system to complete a specific, precisely defined task, and not merely to test anything. By using artificial intelligence, the RoboCT will assist users with various tasks by functioning as a black box to recommend, depending on the job at hand, optimum parameterizations in terms of accessibility and acquisition.
