Master Thesis: Modelling Complex Heterogeneous Multi-Robot Task Collaboration in München bei Fraunhofer Gesellschaft

Safe Intelligence — this forms the core brand of the Fraunhofer Institute for Cognitive Systems IKS. Connected cognitive systems drive innovation in many sectors, for example in autonomous vehicles, medical devices or intelligent automation within industry. They should always take full advantage of the potential offered by artificial intelligence, while remaining demonstrably safe and reliable at the same time. This is why Fraunhofer IKS researches both artificial intelligence and software engineering — we consider resilience and intelligence as part of the same process.

What you will do

The collaboration of multiple heterogeneous robots to complete various complex tasks holds great potential to bring Multi-Robot-Systems (MRS) into real-world applications. Due to their heterogeneity, such systems can work in multiple different scenarios and are resilient to upcoming changes. To achieve the required level of autonomy, the MRS must have a Task Management module that handles incoming tasks and breaks down complex tasks into subtasks for each robot.

Your tasks:

• Based on the references [1] – [4], model a system architecture for multi-robot collaboration on complex tasks.
• Define a UML meta model for autonomous task management of a heterogeneous MRS.
• Integrate and validate the results in a ros2-based simulated environment.

[1] Rizk, Y., Awad, M. & Tunstel, E. (2019). Cooperative Heterogeneous Multi-Robot Systems. ACM Computing Surveys, 52(2), 1–31. doi: 10.1145/3303848

[2] Korsah, G. A., Stentz, A. & Dias, M. I. (2013). A comprehensive taxonomy for multi-robot task allocation. The International Journal of Robotics Research, 32(12), 1495–1512. doi: 10.1177/0278364913496484

[3] Zlot, R. M. (2006). An auction-based approach to complex task allocation for multirobot teams (Doctoral dissertation, Carnegie Mellon University, The Robotics Institute).

[4] Böhm, W., Broy, M., Klein, C., Pohl, K., Rumpe, B., & Schröck, S. (2021). Model-based engineering of collaborative embedded systems: Extensions of the spes methodology (p. 404). Springer Nature.

What you bring to the table

• Student in engineering or equivalent degree
• Knowledge on software systems engineering
• Experience in robotics, ROS2, webots or software architectures is a plus
• Analytical and structured mindset
• Good knowledge of the English language

What you can expect

• Participation in a dynamic team with innovative task areas
• Practical approach to your studies
• Possibility to continue your work as a working student

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Interested? Apply online now. We look forward to getting to know you!

Additional questions will be answered gladly by

Núria Mata, please email to: nuria.mata@iks.fraunhofer.de
If you got further questions, please contact our colleagues from HR via eMail: recruiting@iks.fraunhofer.de

Fraunhofer Institute for Cognitive Systems IKS

Requisition Number: 67482 Application Deadline: