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Sensor-based activity detection

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Fraunhofer IML researches the pallet of the future

Up to now, pallets have primarily fulfilled one task: as load carriers, they move things from A to B. But what if they could be more than just load carriers? The "Pal2Rec" project, which was launched in February 2024, is investigating how Euro pallets can be independently integrated into logistics processes using sensor-based activity recognition.

© Fraunhofer IML

Pallets also generate movement or mobility data, for example through QR codes. Until now, however, these always had to be classified using accompanying information such as camera images. Pal2Rec stands for "Sensor-based logistical activity recognition of Euro pallets". The research project is investigating whether the movement data of pallets can be recognized and interpreted through the use of sensors. This would not only provide a completely new data basis for logistics processes, but also open up entirely new analysis options.

The project is being funded by the Federal Ministry for Digital and Transport (BMDV) with around 181,000 euros as part of the MFund innovation initiative. The Fraunhofer Institute for Material Flow and Logistics IML is researching the project as the joint coordinator together with the Chair of Materials Handling and Warehousing (FLW) at the Technical University of Dortmund until October 2024. The aim is to prove that logistical processes can be interpreted and reproduced using the additional sensor data. To provide this proof, the researchers are equipping a range of demonstrators with the necessary sensors.

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In an experimental setting, they want to find out which sensors are particularly suitable and which data should be collected. In cooperation with industrial partners, the process steps that should be the focus of data collection will be identified. On this basis, the researchers are developing a script that enables the data to be processed and activity to be recognized manually. Finally, the findings will be tested on a large scale under real-life conditions.

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