Robotics belongs to the largest fields of application for control engineering methods. Besides conventional industrial robots, as known from the automobile production, there is an increasing usage of collaborative and mobile robots, for example as vacuum cleaners or autonomous vehicles. Research projects in this area concern the control of movements and forces on the one hand and the planning of paths and trajectories on the other.
Until a few years ago, robots were mainly used in industry for the automation of work processes where speed and accuracy were of primary importance. In contrast, they are now increasingly entering domains that were previously reserved for humans. Modern robots mow the lawn, take over tasks in the care of old and sick people and assist workers in the factory hall without separation by a safety fence.
This poses numerous challenges that call for new approaches and methods which, in addition to the actual task, always keep human safety in mind.
Research projects at the Chair of Automatic Control include the development of collision-free motion planning methods as well as modern approaches to force and compliance control. As part of Festo‘s Bionic Learning Network, a jumping kangaroo (BionicKangaroo) was co-developed and a cooperative load transport with ants (BionicAnts) implemented.
If the position of humans and objects in the workspace is detected by sensors, safety can be improved by collision-free motion planning. The challenges are the high number of degrees of freedom of a robot arm, the complex collision detection and the limited computation time, when the environment changes frequently.
Research at the Chair of Automatic Control concerns on the one hand the method of dynamic roadmaps, which enables global path planning in less than 100 milliseconds, and its efficient extension to dual-arm robots. On the other hand, optimization-based methods are investigated, that allow taking constraints into account, e.g. closed kinematics. The advantages of both approaches can be combined using predictive path-following control.
- Collision-free motion planning for a rotating robot with seven degrees of freedom: Video
- Collision-free motion planning for a dual-arm robot with twelve degrees of freedom: Video
- Motion planning for a dual-arm robot with closed kinematics: Video
- Predictive path-following control for continuous replanning with dynamic roadmaps (IEEE RA-L 2019, DOI der Publikation: 1109/LRA.2019.2929990): Video
When the robot is in contact with its environment, the forces and moments that arise must be taken into account by the control system. 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.
In order to meet the demand for versatile and flexibly applicable control methods, the Chair of Automatic Control develops a model predictive interaction controller. Here, differenct objectives such as position control, direct force control, impedance contorl, or hybrid variants can be realized by a generic control framework. Safety aspects can be explicitly considered by limiting the maximum velocities or forces.
One possible field of application is the generic implementation of assembly tasks. The task can be decomposed into a sequence of motion and interaction primitives, where each primitive is represented by a specific parameterization of the model predictive interaction control. In order to reduce the effort for parameterization, the use of learning-based methods is investigated.
- “Writing on a blackboard with chalk” – Model predictive interaction control for hybrid force/motion control (submitted and accepted at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ): Video
Another field of research is the development of real-time predictive motion planning methods for driving simulators. By using modern nonlinear model predictive control approaches (MPC) the actuator constraints and the future reference values can be directly incorporated into the motion planning, which is not possible when using the classic filter-based approach. This enables a dynamic prepositioning of the simulator and leads to a better exploitation of the simulator potential. Another aspect in this field is the consideration of perception models to take the mechanism of the human motion perception into account.