Optimising intralogistics with AI

Engineering Industry News

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At its production facilities in Barntrup, KEB Automation operates an in-house transport system, AGILOX, which is designed specifically for intralogistics tasks. The AGILOX system comprises of a swarm (union) of smart automated guided vehicles (AGVs) that work collaboratively to transport items around the warehouses.

Despite its sophistication, the AGILOX system isn’t immune to occasional operational or technical failures. When a vehicle stalls, it results in an immediate stop, necessitates human intervention, and ultimately leads to delays in operations. AGILOX constantly generates data regarding vehicle status and orders, providing a valuable opportunity to utilise this data for further analysis. 

In AutoQML – a project that develops solution approaches for linking quantum computing and machine learning – KEB’s primary objective is to devise a machine learning solution capable of monitoring vehicle status and predicting potential failures. This aligns with KEBs larger objective of facilitating the broader transition to quantum computing in the future, by supporting research institutes with practical, real-world applications. 

The machine learning solution to be developed in this project will be seamlessly integrated into the KEB Ecosystem to provide constant surveillance of the AGILOX system’s health status, with the ultimate aim of enhancing intralogistics operations and minimising downtime costs.

Khaled Al-Gumaeiis Senior Data Architect IIoT at KEB. He is the project manager from the KEB side and one of the minds behind the project AutoQML. He comments: “In my role as a data architect, my primary focus lies in designing and developing data policies and infrastructure related to Industrial IoT. However, working closely with data analysts has augmented my understanding of AI and machine learning, particularly as they apply to Industrial IoT data analysis.”

He continues: “Regarding quantum computing, my exposure is currently more theoretical. Nevertheless, I am eager for the opportunity this project presents to apply this theoretical knowledge in a practical context, enhancing the utility in the Industrial sector.”

“In this project, my primary responsibility is to create a data platform that encompasses the collection, transfer, storage and management of AGILOX data, ensuring it is appropriately prepared and accessible for the AutoQML data scientists. Additionally, I bridge the gap between business and technology by translating the business requirements outlined by KEB’s domain experts into technical prerequisites for data analysis. Once the solution is developed, I will oversee its integration into the KEB Ecosystem, coordinating the implementation process to ensure seamless functionality.” 

From this project, KEB expects several benefits, as Khaled Al-Gumaei explains: “On the technical side, we aim to leverage the advantages of automatic machine learning [AutoML] techniques. These techniques will allow us to create AI models without extensive data science experience. Our goal is to discern complex patterns within the AGILOX system, thus optimising our in-house logistics, simplifying maintenance, and importantly, reducing costs.”

Beyond the immediate technical benefits, KEB believes its work also contributes significantly to the larger community. KEB’s mission is to support research efforts focused on translating current industrial application challenges into problems that can be solved using quantum computing. In doing so, it hopes to help drive the wider adoption of advanced technology and contribute to the evolution of digital innovation. 

For more information, please visit www.keb.co.uk or www.autoqml.ai