Cooperative Systems

Vision

How will automation interact with humans in the future?

How to create synergies between humans and machines in the context of Industry 4.0?

The research group Cooperative Systems develops a framework for modeling and control of interactions between humans and machines. The individual strengths of human and machine are combined to achieve high performance systems, ready to meet future challenges of automatization. The fields of applications are e.g. Advanced Driver Assistance Systems, Robotics, Medical Technologies and Aerospace Engineering.

Cooperative Control Loop

Modeling and Identification

The modeling of cooperative systems forms the basis of automation design for cooperative scenarios. In this context, uncertainties in perception and action need to be considered. Furthermore, semantics enable a strategic description of the interaction. Moreover, the identification of human behavior is essential in automation design.

Control Synthesis

Automation design in a cooperative scenario needs to be capable of a dynamic allocation of authority. Furthermore, it requires the ability to negotiate a common goal with the human. One approach to control cooperative systems is based on game theory and Model Predictive Control (MPC). In order to achieve real-time control, motion primitives are examined.

Experiments

	Motion Tracking is used in various scenarios to measure human motion and validate identification methods.
	An Advanced Driving Simulator with haptic feedback human machine interfaces was developed at the IRS. It is used to validate cooperative control methods in the context of advanced driver assistance systems.
	A newly developed Ball-on-Plate experiment will be used to apply cooperative identification and control methods in a highly dynamical scenario.

Staff

	Balint Varga Head of Research Group Research Interest: Embodied and Human-Centric Teleoperation
	Christian Braun Research Associate Research Interest: Shared control of heterogenous robot swarms		Julian Schneider Research Associate Research Interest: Multi level connection for the consistent design of cooperative human-machine systems
	Philipp Karg Research Associate Research Interest: Modeling and Identification in Cooperative Human-Machine Scenarios		Sean Kille Research Associate Research Interest:
	Karl Handwerker Research Associate Research Interest:		Felix Thömmes Research Associate Research Interest:
	Lucas Günther Research Associate Research Interest:

Bachelor- and Master Students

Zihao Zhang

Master Thesis

Untersuchung von Reinforcement-Learning-Ansätzen für Shared Control bei chirurgischen Robotersystemen

Lukas Krauß