Image analysis of topology changes for laser-based soft material processing

基于激光的材料加工是一个新兴领域，主要是切割，但也包括焊接，钎焊，烧结和缝纫等连接技术或切除程序，如消融和雕刻。基于激光的软材料加工的应用领域包括纺织工业的织物处理，农业中的除草等植物处理，以及生物学和医学中的活组织操作。这些领域的进一步自动化将节省成本，优化流程，并可能减轻饥饿和疾病。这项发现补助金的研究将集中在基于激光的软材料加工自动化的未解决问题上。创建激光切割会在软材料中产生拓扑变化。对于自主切割，需要补偿材料的潜在变形和变化，包括对实际拓扑及其进展的算法考虑。 一般来说，软材料的操作往往会导致形态和拓扑结构的变化。虽然这些形态变化与变形是等价的，但拓扑结构的变化可以理解为材料内部孔的发展。本研究将利用三维视觉方法检测并补偿激光加工引起的拓扑变化。就实用性而言，激光加工具有优点，即发生修改的区域不会被较大的物体（例如刀片）覆盖。总体假设是，实时结合拓扑结构的变化将允许对软材料变形进行鲁棒跟踪，并在自主激光加工期间产生先进的结果。为了证明这一假设，这项发现补助金寻求回答以下科学关键问题（SKQ）：A -不同类型的拓扑变化可以自动识别吗？B -如何通过计算区分拓扑和形态变化？C -可以利用哪些计算机视觉方法来检测软材料上/内的拓扑变化及其空间位置？D -如何在计算机视觉和激光加工方法之间闭环时考虑形态和拓扑变化？E -在周围软材料进一步变形或不变形的情况下，检测拓扑变化位置的精度和延迟是多少？该项目被细分为四个目标来回答SKQ：目标1关心软材料激光加工的拓扑和形态变化的数据集的生成。目标2包括该建议的核心工作，并导致基于视觉的拓扑和形态变化及其特定位置的识别的算法原型。目标3是将这些算法转化为闭环机器人系统，目标4量化了系统特性和激光加工结果的精度。

Laser-based material processing is an emerging field, predominantly cutting, but also joining technologies such as welding, soldering, sintering and sewing or removal procedures like ablation and engraving. Application areas for laser-based soft material processing include fabric handling for textile industry, treatment of plants such as weeding in agriculture, as well as living tissue manipulation in biology and medicine. Further automation in these areas will save costs, optimize processes and may alleviate hunger and disease. The research in this Discovery Grant will focus on unsolved aspects for automation of laser-based soft material processing. Creation of a laser cut produces a topological change in soft material. For autonomous cuts, potential deformation and changes of the material need to be compensated for, including algorithmic consideration of the actual topology and its progression. In general, manipulation of soft material often results in changes of morphology and topology. While those morphological changes are equivalent with deformations, changes of topology can be understood as the development of holes inside the material. This research will detect and compensate topological changes induced by laser processing with 3D vision methods. In terms of practicability, laser processing has the advantage, that the area where the modification happens is not covered by larger objects (e.g. a knife blade). The overall hypothesis is that incorporating the change of topology in real-time will allow for a robust tracking of soft material deformation and lead to advanced results during autonomous laser processing. To prove the hypothesis, this Discovery Grant seeks in answering the following scientific key questions (SKQ): A -Can different types of topological changes be identified automatically? B -How can topological and morphological changes be differentiated computationally? C -Which computer vision methods can be leveraged to detect the change of topology and its spatial location on/inside the soft material? D -How to take morphological and topological changes into account while closing the loop between computer vision and laser processing methods? E -What is the accuracy and latency of detection of a topological change location with or without further deformation of the surrounding soft material? This project is sub-divided into four aims to answer the SKQ: Aim 1 cares about generation of a dataset on topological and morphological changes of soft material laser processing. Aim 2 include the core work of this proposal and result in algorithmic prototypes of the vision-based recognition of the topological and morphological changes and their specific locations. Aim 3 is translating theses algorithms into a closed-loop robotic system and Aim 4 quantifies the system characteristics and resulting accuracy in terms of the laser processing results.