Intelligent Parts Recognition and Navigation using Robotic Arms (IPRN)**

使用机械臂进行智能零件识别和导航 (IPRN)**

• 批准号: 535882-2018
• 负责人: Cheng, LinOiIrene
• 金额: $ 1.82万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=647457
• 关键词: Intelligent; Parts; Recognition; Navigation; using

Parts production is a necessary process in manufacturing. The raw machine parts have to be identified and fitted into specific locations for cutting, drilling, grinding and so on. Currently, this process is mainly manual and labour intensive. Due to the high labour cost in North America, the parts production process is often out-sourced to cheaper labour markets outside Canada. Rational Robotics is an Edmonton based company, which specializes in developing robotic arms. The goal of this proposal is to automate the parts production process, using robotic operation and applying state-of-the-art human-computer interaction, computer vision, image analysis and pattern recognition techniques. Instead of identifying and inspecting parts manually, cameras will be deployed to capture multiple views. The captured images will be analyzed to match specific profiles in a parts database. The challenge is to recognize a unique part using minimum number of views in real-time. We will compare traditional vision-based algorithms with machine learning methods, and finally design an efficient approach to address this research problem. The proposed tasks will support a robotic automation pipeline: picking up an item from a pile of parts, viewing and recognizing, and fitting into the correct machine position. When the machining is done, an inspection step will be performed. A recovery procedure is available if needed. By embedding the ENGAGE outcome into Rational Robotics' already developed robotic arms, the company can accelerate a new market not only in parts production, but also other robotic applications. Without the need to out-source labour intensive jobs outside the country, Canada can benefit economically and technologically.

零件生产是制造业中必不可少的工序。机器零件的原材料必须经过识别，并安装到特定的位置，以便进行切割、钻孔、磨削等。目前，这一过程主要是人工和劳动密集型的。由于北美劳动力成本高，零件生产过程往往外包给加拿大以外的廉价劳动力市场。Rational Robotics是一家位于埃德蒙顿的公司，专门开发机器人手臂。该提案的目标是使用机器人操作并应用最先进的人机交互、计算机视觉、图像分析和模式识别技术，实现零件生产过程的自动化。取代人工识别和检查零件，相机将被部署来捕捉多个视图。将分析捕获的图像以匹配零件数据库中的特定配置文件。挑战在于使用最少数量的视图实时识别独特的零件。我们将比较传统的基于视觉的算法和机器学习方法，并最终设计一种有效的方法来解决这个研究问题。拟议的任务将支持机器人自动化流水线：从一堆零件中拾取物品，查看和识别，并安装到正确的机器位置。加工完成后，将执行检查步骤。如果需要，可以使用恢复程序。通过将ENGAGE结果嵌入到Rational Robotics已经开发的机器人手臂中，该公司不仅可以加速零件生产，还可以加速其他机器人应用的新市场。由于不需要将劳动密集型工作外包到国外，加拿大可以在经济和技术上受益。