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Machining-based coverage path planning for automated structural inspection

机译:基于机加工的覆盖路径规划,用于自动结构检查

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摘要

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets.
机译:机器人自动执行的无损评估检查的自动化与传统制造加工具有许多目标。本文通过采用计算机辅助制图(CAD)/计算机辅助制造(CAM)启发的路径规划来实现对新型机器人的控制,提出了一种新的硬件和软件系统,该系统可以自动对具有多个障碍的大型区域进行厚度映射移动式机器人测厚仪。定制的后处理器通过机器人运动学控制从CAM数字代码提供必要的转换,以组合和自动化整个过程。本文介绍了针对任何移动机器人平台实施此方法的一般步骤,并在这种情况下应用于新颖的厚度映射爬虫。在运动跟踪单元内的室内模拟检查场景中评估了系统的检查能力,以提供定量的性能数据以提高位置精度。模拟钢板样品板上腐蚀特征的多个厚度缺陷与检查过程中应避免的障碍结合在一起。对于标称厚度为10 mm的2m²碳钢样品,观察到的最小厚度映射误差为0.21 mm,平均路径误差为4.41 mm。这种自动化方法的潜力在区域覆盖的可重复性,避开障碍物和减少路径重叠方面具有优势,所有这些都直接提高了任务效率,并减少了大型结构资产的检查时间。

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