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A robot arm digital twin utilising reinforcement learning
Computers & Graphics ( IF 2.5 ) Pub Date : 2021-02-04 , DOI: 10.1016/j.cag.2021.01.011
Marius Matulis , Carlo Harvey

For many industry contexts, the implementation of Artificial Intelligence (AI) has contributed to what has become known as the fourth industrial revolution or “Industry 4.0” and creates an opportunity to deliver significant benefit to both businesses and their stakeholders. Robot arms are one of the most common devices utilised in manufacturing and industrial processes, used for a wide variety of automation tasks on, for example, a factory floor but the effective use of these devices requires AI to be appropriately trained. One approach to support AI training of these devices is the use of a “Digital Twin”. There are, however, a number of challenges that exist within this domain, in particular, success depends upon the ability to collect data of what are considered as observations within the environment and the application of one or many trained AI policies to the task that is to be completed. This project presents a case-study of creating and training a Robot Arm Digital Twin as an approach for AI training in a virtual space and applying this simulation learning within physical space. A virtual space, created using Unity (a contemporary Game Engine), incorporating a virtual robot arm was linked to a physical space, being a 3D printed replica of the virtual space and robot arm. These linked environments were applied to solve a task and provide training for an AI model. The contribution of this work is to provide guidance on training protocols for a digital twin together with details of the necessary architecture to support effective simulation in a virtual space through the use of Tensorflow and hyperparameter tuning. It provides an approach to addressing the mapping of learning in the virtual domain to the physical robot twin.



中文翻译:

利用强化学习的机械手臂数字孪生

在许多行业环境中,人工智能(AI)的实施推动了第四次工业革命或“工业4.0”的发展,并创造了为企业及其利益相关者带来重大利益的机会。机械臂是制造和工业过程中最常用的设备之一,用于例如工厂车间的各种自动化任务,但是有效使用这些设备需要对AI进行适当的培训。支持这些设备的AI训练的一种方法是使用“数字孪生”。但是,在这一领域内存在许多挑战,特别是,成功取决于在环境中收集被视为观测数据的能力以及将一项或多项受过训练的AI策略应用于要完成的任务的能力。该项目提供了一个案例研究,该案例研究创建和训练Robot Arm Digital Twin,将其作为在虚拟空间中进行AI训练并将此模拟学习应用于物理空间的方法。使用Unity(现代游戏引擎)结合虚拟机器人手臂创建的虚拟空间链接到物理空间,该物理空间是虚拟空间和机器人手臂的3D打印副本。这些链接的环境用于解决任务并为AI模型提供培训。这项工作的目的是为数字双胞胎的训练协议提供指导,并提供必要架构的详细信息,以通过使用Tensorflow和超参数调整来支持虚拟空间中的有效仿真。它提供了一种解决虚拟域中的学习到物理机器人孪生的映射的方法。

更新日期:2021-02-24
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