Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/111744
Title: | Intralogistics application with a fleet of robots on a private 5G campus network |
Author(s): | Kalter, Marc Karbach, Dennis Schellenberger, Christian Schöneberg, Eric Vierling, Axel Schotten, Hans D. Görges, Daniel Berns, Karsten |
Issue Date: | 2023 |
Type: | Conference object |
Language: | English |
URN: | urn:nbn:de:gbv:ma9:1-1981185920-1137019 |
Subjects: | 5G Intralogistics Private network Robot fleet Teleoperator ROS2 Controlcenter |
Abstract: | This paper presents the concept and the current state of implementation of a semi- autonomous robot fleet for logistics applications in a campus environment. The communication is realized via a private 5G SA network. The robot fleet performs its logistics tasks semi-autonomously on campus and can deliver mail or parcels, for example. Sensor data (GPS, camera images, 2D and 3D laser scanners, ...) is sent to a central computing unit in the control center via the 5G interface to analyze and store live data and influence the robot’s actions at real time to save costs of the robot and conserve energy to increase operating time. The operator in the control center can intervene in unusual situations at any time and remotely control the robots via 5G. The described system is being tested with a fixed private 5G SA network and a nomadic 5G SA network as public cellular networks are not performant enough in regards to low latency and upload bandwidth. The nomadic network approach opens up further application scenarios such as company premises or events. The system has so far been built and tested with one robot. The expansion of the robot fleet with different platforms is currently in progress. |
URI: | https://opendata.uni-halle.de//handle/1981185920/113701 http://dx.doi.org/10.25673/111744 |
Open Access: | Open access publication |
License: | (CC BY-SA 4.0) Creative Commons Attribution ShareAlike 4.0 |
Appears in Collections: | Fakultät für Elektrotechnik und Informationstechnik (OA) |
Files in This Item:
File | Description | Size | Format | |
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19_KommA2023_P-11_Kalter et al..pdf | Paper | 4.84 MB | Adobe PDF | View/Open |