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SPIRIT - A Software Framework for the Efficient Setup of Industrial Inspection Robots

机译:SPIRIT-高效设置工业检查机器人的软件框架

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SPIRIT aims to develop an “inspection skill” for robots that takes the step from programming of complex inspection tasks to configuring such tasks. Result of the project is a software framework that includes an “offline framework” with features such as model-based automatic coverage planning for complex parts, automatic robot program generation and an “inline framework” that deals with sensor data mapping to transfer sensor measurements to the 3D object model. At the heart of the project is an accurate process-specific model that represents the sensor data acquisition process. This representation is sufficiently accurate to allow automatic planning in off-line settings using simulated workcells and then reproducing the inspection procedure on the real one, with some adaptations and corrections. More in detail, the “offline framework” will include a generic interface to allow the easy exchange of process models (for different inspection technologies), of the CAD model of the part (for a different type of product to be inspected) or of the work-cell model (for a different robot kinematic structure). The generic “inline framework” will provide the backbone for the execution of the actual inspection process. Relying on such a proven frameworks will reduce the risks of implementing complex inspection tasks and thus help the deployment of inspection robots. In this work initial results from the two main use cases are presented, they consist of inspection tasks for the automotive and aerospace industry respectively.
机译:SPIRIT的目标是为机器人开发一种“检查技能”,从编程复杂的检查任务到配置此类任务的步骤。该项目的结果是一个软件框架,其中包括一个“离线框架”,其功能包括基于模型的复杂零件自动覆盖计划,自动机器人程序生成以及一个“在线框架”,该框架处理传感器数据映射以将传感器测量值传输至3D对象模型。该项目的核心是一个精确的特定于过程的模型,该模型代表了传感器数据采集过程。这种表示方式足够精确,可以使用模拟工作单元在离线设置中进行自动计划,然后在实际情况下通过一些调整和更正来重现检查程序。更详细地讲,“离线框架”将包括一个通用接口,以允许轻松交换过程模型(用于不同的检查技术),零件的CAD模型(用于要检查的不同类型的产品)或零件的CAD模型。工作单元模型(用于不同的机器人运动学结构)。通用的“内联框架”将为执行实际检查过程提供基础。依靠这种经过验证的框架将降低执行复杂检查任务的风险,从而有助于部署检查机器人。在这项工作中,给出了两个主要用例的初步结果,它们分别包括汽车和航空航天业的检查任务。

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