MA – Swarm Navigation of Autonomous Vehicles in Industry

Warehouses and factories become more and more crowded. The number of autonomous vehicles replacing static conveyor belts for transportation is increasing which in turn amplifies the overall system complexity. Navigation paths have to be aligned among the agents and plans have to be revised due to unanticipated disturbances from the environment. State-of-the-art centralized systems for coordination exhibit inherent limitations: By design they introduce a single point of failure in terms of infrastructure and compute availability which needs investment into redundant equipment protecting against system outages. Also coordination problems are typically treated globally which are difficult to solve and thus introduce non-negligible latencies in many-agent systems. In this thesis we would like to explore a system which dispenses with central services and pre-established infrastructure. The agents instead bring their own infrastructure and communicate across direct device-to-device channels. The focus will be put on the decentral alignment of planned paths in an industrial environment. The algorithms shall be tested in a simulated environment as well as on real hardware. Siemens has developed for this purpose a learning platform named IOTBot which is based on the Simatic IOT2050 as a control unit. Multiple IOTBots will be used in this thesis for putting the developed algorithms to a realistic test.

Your tasks:
• Familiarize yourself with ROS2 and the base-AGV ‘IOTBot’
• Evaluate the challenges your team of robots should be able to solve
• Split up the needed functionalities and appoint theme-based members
• Create a concept for each robot and implement a part of the ROS2 applications

Requirements:
• Basic or advanced programming skills
• Familiar with (embedded) Linux environments
• Good English communication frame
• Contribution and promotion of your own work (maker fair, Open Source Contribution, wiki)

Offerings:
• Working in the SIEMENS Academic Research Team: The emphasis is on cooperation and exchange
• Results are developed in collaboration within a student environment
• Each student is supported in the success of their personal project and can get involved across their own assignments

For further information inquire below:

 

Kategorien:

Forschungsbereich:

Robotik

Art der Arbeit:

Masterarbeit

Studiengang:

Informatik, Maschinenbau, Mechatronik

Technologiefeld:

Fertigungsregelung und Intralogistik

Kontakt:

Maximilian Zwingel, M. Sc.

Department Maschinenbau (MB)
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS, Prof. Franke)


Christian Hofmann, M. Sc.

Department Maschinenbau (MB)
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS, Prof. Franke)