Robotics

Robotics deals in general with machines that can assist in or perform the execution of tasks such as assembly or machining by industrial robots. Research projects in this area concern, for example, the control of motions and forces in human-robot interaction as well as the planning of paths and trajectories for mobile and collaborative robots.

Contact

Dr.-Ing. Andreas Völz
Tel.: +49 9131 85-61036
E-Mail | Homepage

Prof. Dr.-Ing. Knut Graichen
Tel.: +49 9131 85-27127
E-Mail | Homepage

Robots should move and interact with humans as efficiently as possible. If this includes high velocities or heavy payloads, the nonlinear rigid body dynamics as well as the input constraints need to be taken into account, which leads to a computationally demanding optimization problem. On the other hand, when the robot is in contact with its environment, the rigid body dynamics are often less relevant, but the forces and torques that arise must be considered by the control system. This is especially important for safe human-robot interaction. Here, the challenges include the contact modelling (e.g. stiffness and friction), the safe handling of contact loss, or the selection of controller parameters for different applications. Current research considers model predictive interaction control (MPIC), which refers to MPC with explicit prediction of contact forces and torques, as well as the development of specialized algorithms for solving optimization problems with rigid body dynamics.

Cooperative dual-arm manipulation
Robot-environment interaction control
XPlanar system and Allegro hand

Besides controlling motions, also the planning of paths (geometric description) and trajectories (time information) is a relevant problem for many types of robots. In particular for mobile and collaborative robots, motions should be planned in such a way that they do not cause self-collisions or collisions with obstacles in the environment. Global planners iteratively build a search structure that explores the space of possible motions, whereas local planners only search in the neighborhood of an initial solution. In order to efficiently find high-quality solutions, it is necessary to combine the advantages of both global and local planning methods. Further difficulties arise in dynamic environments, where the future motion of obstacles needs to be predicted or for car-like robots, where the non-holonomic kinematics need to be considered.

Boston Dynamics Spot
Dynamic obstacle detection
Collision-free motion planning

The Chair is equipped with a mobile dual-arm robot as well as a motion capturing system that have been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the major instrumentation proposal INST 90/1167-1 FUGG. The system enables the experimental validation of planning and control methods for mobile manipulation in workspaces shared with humans. For more information contact Prof. Dr.-Ing. Knut Graichen or Dr.-Ing. Andreas Völz.

Related projects since 2023


Term: 1. February 2024 - 31. January 2026
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
Project leader:

Prof. Dr.-Ing. Knut Graichen

Chair holder

→ More information

Term: 1. July 2024 - 31. December 2025
Funding source: Industrie
Project leader:

Prof. Dr.-Ing. Knut Graichen

Chair holder

→ More information

Term: 1. November 2024 - 30. April 2026
Funding source: Industrie
Project leader:

Prof. Dr.-Ing. Knut Graichen

Chair holder

→ More information

Term: 1. January 2026 - 31. December 2028
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader: ,

Prof. Dr.-Ing. Knut Graichen

Chair holder

Robotic hands are often motivated by the fact that they can be used to perform a wide range of manipulation tasks. The extension to a robotic hand-arm system (e.g., as part of a humanoid robot) makes it possible to significantly reduce the workload of people in shared workspaces. Clear examples of this can be found both in industrial manufacturing processes (pick and place, assembly, machining, etc.) and in everyday human life (opening doors, cutting food, handling objects, etc.). In addition to complicated movements, such tasks require the precise adjustment of forces. Nevertheless, they are usually performed by humans or various highly specialized machines. Alternatively, the environment could be adapted to the technology, but this is hardly practicable. In addition to safety, a particular challenge in shared workspaces is the acceptance of the robot system by the people present. An important aspect of this is that people must be able to recognize and anticipate the robot's movements intuitively. The research project aims to develop a synergy-based model predictive control (MPC) for robotic hand-arm systems that is integrated into a modular control architecture. The challenges of numerous actuated degrees of freedom are considered and the methods for null-space control are co-developed. With the help of a model-predictive approach, the aim is to achieve a modular architecture that also integrates task planning. Various approaches are being developed to efficiently integrate linear and non-linear kinematic synergies into a modular MPC formulation. Therein, the synergies are intended to reduce the complexity and thus the computation effort on the one hand and to realize human-like motions on the other. This approach also aims to increase the human acceptance of such systems.

→ More information

Term: 1. January 2026 - 31. December 2028
Funding source: Industrie
Project leader:

Prof. Dr.-Ing. Knut Graichen

Chair holder

→ More information


Related publications