Motion Control

Focus of our research:

  • Motion planning, control and optimization
  • Synchronization and interconnection of several robots in the overall concept
  • Balance of data-driven and model-based methods

Motion control is an essential subject for automation and therefore has a high impact on productivity, quality and efficiency of a robotic application.

Especially flexible applications and environments pose a challenge in motion planning. In particular, the time required for manual robot programming increases due to a multitude of possibilities and parameterizations. In order to reduce this effort and at the same time achieve high efficiency, we are investigating automatic motion planning and trajectory optimization. In this context we focus on application-oriented algorithms and the analysis of future-oriented performance indicators such as economic and ecological efficiency.

A further challenge for motion planning arises in complex production chains with several processes and multiple dependencies. Therefore, we consider the synchronization and interconnection of several robotic systems in the overall concept and explore the utilization of synergy effects, for example by coordinating manipulators and mobile industrial robots.

The foundation for our research in the field of motion control are model-based representations of the robotic applications. In order to improve the model accuracy and to compensate characteristics that are difficult to model such as compliance, friction and wear, we do research on the balance of data-driven and model-based methods. Thereby, we combine data-driven approaches for learning and updating models with the use of efficient and robust model-based algorithms.