M. Sc. Nina Majer

  • FZI Forschungszentrum Informatik
    Embedded Systems and Sensors Engineering (ESS)
    Haid-und-Neu Str. 10-14
    76131 Karlsruhe

Curriculum Vitae

Studies of Mechatronics and Information Technology at the Karlsruhe Institute of Technology (KIT) and the Pennsylvania State University (PSU) with the field of specification in Control in Mechatronics. 2020 Master's thesis "Safely Learning Predictive Adaptive Cruise Control for Highly Automated Vehicles“ at the FZI.                                                                                                                                       
Since April 2021 Research scientist at the department Control in Information Technology (CIT) in the research division Embedded Systems and Sensors Engineering (ESS) at the FZI.


Cooperative Trajectory Planning in mobile robotics

The motion of multiple mobile robots in a shared operation area can lead to intersection situations that cannot be resolved by individual and uncoordinated trajectory planners. In present automated production and logistics systems, these kinds of scenarios are primarily coordinated by priority-based methods. This coordination often leads to a deceleration of the traffic flow and in extreme case, to a Deadlock. The global optimal solution of the multi-robot trajectory planning problem is not reached if the total time needed to resolve the intersection situation is considered as the global performance criterion.
The calculation of the global optimal solution by considering the coupled configuration space of the robots comes with a high computational effort. To meet real-time capability, the coupled planning problem can be solved on a powerful computer that serves as a coordinating unit. This approach, however, creates a dependency between the robots and the central coordination unit. Moreover, installing powerful computers into the infrastructure close to locations where potentially intersection scenarios could occur, is neither practical nor economic.
Therefore, a decentralized and cooperative trajectory planner to efficiently and fluently resolve intersection scenarios shall be developed. A preferably small percentage of explicit communication between the robots should be required.



Safely Learning Model Predictive Control with Time-Variant State Constraints and its Application to Motion Planning
Kohrer, L.; Majer, N.; Schwab, S.; Hohmann, S.
2021. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC): 19-22 September 2021, Indianapolis, IN, USA, 755–762, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ITSC48978.2021.9564934