生产、仓储和发货——无论在任何地方制造、分拣或包装货物，均会涉及到拾取流程。在此过程中，机器人通常用于将单独的货物从板条箱中取出并进行重新分门别类。作为 FLAIROP 项目的一部分，Festo 携手德国和加拿大的合作伙伴研究如何利用分布式人工智能算法提升拾取机器人的智能化水平。他们正在研究如何使用来自多个工作站、工厂或公司的训练数据，而不必公开敏感的公司数据。
卡尔斯鲁厄理工学院 (KIT) 材料搬运与物流研究所 (IFL) 的 Jonathan Auberle 表示：“我们正在研究如何使用来自多个位置的各种训练数据和人工智能算法研发一些解决方案，这些解决方案比使用只来自一个机器人的数据所研发的解决方案更稳健、更高效。”
在此过程中，多个拾取工作站的自控机器人抓取并移动物品，以对它们进行分配。这些机器人在各个工作站练习拾放完全不同的物品。训练的最终效果是，它们能够从尚不熟悉的其他工作站抓取物品。卡尔斯鲁厄理工学院的 Jonathan Auberle 表示：“在工业环境中使用分布式学习，也称为‘联邦学习’，我们能够在数据量和数据安全
Until now, federated learning has been used predominantly for analysing images in the medical sector, where the protection of patient data is naturally a particularly high priority. There is therefore no exchange of training data such as images or grasping points for training the artificial neural network. Only parts of the stored knowledge are sent to a central server – the local weights of the neural network that indicate how strongly one neuron is linked to another. The weights from all the stations are collected in the central server and are optimised with the help of various criteria. The improved version is then fed back to the local stations and the process repeats itself.
The aim is to develop new, better-performing algorithms that will enable the robust use of artificial intelligence for Industry and Logistics 4.0 while complying with data protection guidelines.
"In the FLAIROP research project, we are developing new ways for robots to learn from each other, without sharing sensitive data and company secrets. This has two major benefits: we protect our customers’ data and we gain speed because the robots can perform many tasks more quickly. The collaborative robots can thus support production workers with repetitive, difficult and tiring tasks, for example," says Jan Seyler, Head of Advanced Develop. Analytics and Control at Festo.
"DarwinAI is thrilled to be able to make our Explainable (XAI) platform available to the FLAIROP project and pleased to work with such reputable Canadian and German academic organisations and our industry partner, Festo. We hope that our XAI technology will enable high-value human-in-the-loop processes for this exciting project, which represents an important aspect of our offer alongside our novel approach to federated learning. Because of our roots in academic research, we are enthusiastic about this collaboration and the industrial benefits of our new approach for a range of manufacturing customers," says Sheldon Fernandez, CEO of DarwinAI.
"The University of Waterloo is very excited to be working with the Karlsruhe Institute of Technology and a global leader in industrial automation such as Festo to bring the next generation of trustworthy artificial intelligence to manufacturing," says Dr Alexander Wong, Co-Director of the Vision and Image Processing Research Group, University of Waterloo, and Chief Scientist at DarwinAI.
"By harnessing DarwinAI’s Explainable AI (XAI) and federated learning, we can create AI solutions that will help support factory workers in their daily production tasks to maximise efficiency, productivity and safety."