The overall goal of Knowledge4Retail is the development of an open-source platform (K4R platform) for complex AI- and robotics applications in retail. At its core, the platform provides so-called semantic digital twins, which represent a digital copy of a retail store. Semantic digital twins allow the construction of realistic digital worlds, which can be annotated with machine readable background knowledge and linked to other information systems. In this way they provide a powerful basis for AI- and robotics applications in retail. The potential of the K4R platform will be demonstrated in four representative application scenarios.
|Duration:||01.01.2020 till 31.12.2022|
|Donee:||German Research Center for Artificial Intelligence GmbH|
Federal Ministry for Economic Affairs and Energy
German Aerospace Center e.V.
team neusta GmbH,
Allgeier Consulting Services GmbH,
neusta software development West GmbH,
dm-drogerie markt GmbH + Co. KG,
Technische Universität München,
Kaptura GmbH & Co KG,
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.,
EHI Retail Institute GmbH
|Application Field:||Logistics, Production and Consumer|
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Flexible High-Lift Wing Assembly with a Digital and Efficient High-Rate Production in the Industry 4.0 (01.2018- 03.2021)
Robot Construction KitMARS
Machina Arte Robotum Simulans
In order to be competitive with respect to international online-sellers, retailers have to combine competence and customer confidence with the possibilities of digitalization. In a future retail store, employees are providing advice to customers, while robotic systems are in charge of stock-taking, replenishing and collecting scattered items. Smartphone apps direct the customer to the desired goods and answer their queries related to the assortment. Finally, the technology supports visually impaired or disabled people with shopping.
In order to make full use of the possibilities of digitalization in retail, detailed and comprehensive models of the stores, the selling processes and operation sequences are required. Within Knowlegde4Retail, these are provided via an open-source platform (K4R platform), which serves as basis for complex AI- and robotics applications in a retail branch. As a core technology, sematic digital twins will be developed within the project. Sematic digital twins are a generic, machine-readable format for digital representation of retail stores. They allow the construction of realistic digital worlds and enable a variety of novel AI- and robotics applications in retail.
The potential of the K4R platform will be illustrated in four representative applications scenarios:
- Intelligent intralogistics for retail stores
- Strategic trade marketing for setting up highly customized stores
- Service robotics for the support of store employees
- Internet of Things (IoT) – Connecting an intelligent fridge
Within Knowledge4Retail, the DFKI develops the application “Service robotics for the support of store employees” as an example for usage of the K4R knowledge processing services. For this purpose, a mobile service robot for the support of retail intralogistics will be developed. This robot can be seamlessly integrated into the K4R platform and exchange knowledge. Supportive activities include autonomous transport of goods, stock-check, as well as perception of the store environment. In doing so, the robot extends and improves its capabilities with the help of background knowledge provided by the K4R platform. On the one hand, by knowing locations of goods or shelves, availability of employees or other constraints (e.g. urgency, order of unloading, …) actions and routes can be optimally planned and adapted in case of changes in the environment. On the other hand the system will be able to update the store representation in the digital twin by perceiving the environment through its integrated sensors and comparing it to the current digital representation. This bilateral exchange of knowledge with the digital twin helps making intralogistics more efficient and provides ergonomic benefits to the employees.