计算机视觉在工业锂离子电池模组拆卸中的应用

Eduard Gerlitz, Louis-Elias Enslin, Jürgen Fleischer
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引用次数: 0

摘要

基于经济和安全的考虑,自动化机器人辅助拆卸是实现锂离子电池模块柔性拆卸的必要条件。在这种情况下,用于规划过程的CAD模型是非常有益的。由于锂离子电池呼吸的几何不确定性以及组件公差的存在,强调了基于传感器的检测方法的重要性,以确定电池模块的实际状态,这对于确保自动化和可靠的拆卸过程至关重要。在本文中,我们提出了一种基于3D相机的变形电池模块点定位方法,有助于识别机器人辅助拆卸单元中铣削操作的支撑点。这种分离操作计划采用了CAD模型,而我们引入的计算机视觉“数据处理流水线”——系统的一系列处理步骤——弥补了CAD模型与实际电池模块之间的差距。这涉及到使用3D相机捕获模块,随后将其点与CAD模型的点注册。该过程的核心是两种算法:贝叶斯相干点漂移(BCPD)算法确保精确的非刚性配准,而TEASER++有助于减少计算时间。我们通过严格的测试和指标证明了这些组合算法在流水线中的有效性,证明通过调整点密度可以实现精度和计算速度之间的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computer vision application for industrial Li-ion battery module disassembly
Abstract Automated robot-assisted disassembly is essential for the flexible disassembly of Li-ion battery modules for economic and safety reasons. In such a case, a CAD model for the planning process is of immense benefit. The geometric uncertainties due to the breathing of the Li-ion cells as well as the presence of component tolerances underline the importance of a sensor-based detection approach to determine the actual state of the battery module, which is crucial to ensure an automated and reliable disassembly process. In this paper, we present a method for 3D camera-based localization of points on deformed battery modules, aiding in identifying support points for milling operations in robot-assisted disassembly cells. This separation operation planning employs a CAD model, and our introduced computer vision “data processing pipeline”—a systematic series of processing steps—bridges the gap between the CAD model and the actual battery module. This involves capturing the module using a 3D camera and subsequently registering its points with the CAD model’s points. Central to this process are two algorithms: The Bayesian Coherent Point Drift (BCPD) algorithm ensures accurate non-rigid registration, while TEASER++ aids in reducing computational time. We demonstrate the effectiveness of these combined algorithms in our pipeline through rigorous testing and metrics, evidencing that a balance between accuracy and computational speed can be attained by adjusting point density.
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