Dual-armrobots offer a high potential for automation technology, as they canbe used to implement tasks that are not possible with one arm alone.This includes in particular the manipulation of large or heavyobjects that exceed the payload of a single arm. Illustrativeexamples are the movement of beverage crates, long boards or pipes,which are also preferably grasped by humans with both hands.
However,cooperative manipulation is particularly challenging, because botharms and the grasped object form a closed kinematic chain. Thecorresponding constraints reduce the number of degrees of freedom andmust be taken into account on the levels of control, trajectory andpath planning. Conversely, the system has the advantage that the loadcan be flexibly distributed on both arms due to the redundantactuators. This is especially crucial for heavy objects, since it isthe only way to comply with actuator torque constraints. The firstgoal of the research project is therefore the development of adynamic load distribution that explicitly takes actuator constraintsinto account and is thus suited for high payloads. To this end, anoptimization-based approach is pursued with a focus on efficiency andreal-time capability.
Moreover,this load distribution must be taken into account on all systemlevels, since otherwise large payloads can lead to the situation thatno admissible trajectory can be computed for a path or that atrajectory is not executable by the controller. Consequently, thesecond goal is the consistent consideration of the dynamic loaddistribution. On the control level, this includes not only theisolated solution of the optimal load distribution in each samplingstep, but also the approach of a forward-looking model predictivecontroller. For trajectory planning, on the one hand, a time-optimaltrajectory generation with subordinate solution of the dynamic loaddistribution and, on the other hand, the extension of the modelpredictive controller to a predictive path-following controller shallbe investigated. Furthermore, a path planner for dual-arm robots willbe developed for the first time, which explicitly considers thepayload and can be extended in a modular manner to take collisions aswell as the additional degrees of freedom of a mobile base intoaccount.
Thethird goal is the extensive experimental validation of the control,trajectory and path planning methods in order to practicallydemonstrate the potential of cooperative manipulation with dual-armrobots. For this purpose, a mobile dual-arm robot with additionalmotion capturing system from the DFG major instrumentation proposal438833210 is available at the Chair of Automatic Control. Inparticular, movements with large and heavy objects shall beperformed, whose mass is in the order of magnitude of the combinedmaximum payload of both arms.
Our research focuses on the model & control design, analysis, and optimization of dynamical systems from different domains including robotics and human-machine interaction. It is also important for us to bring control and AI related research into practice by closely cooperating with industry, for instance from the automotive domain, robotics and process automation.
Research projects
Energy-Efficient Electro-Photonic Integrated Circuits for High-Performance Computing
(Third Party Funds Group – Overall project)
Funding source: Bayerische Forschungsstiftung
Advanced monitoring and optimization for robotic strain wave gears
(Third Party Funds Single)
Funding source: Industrie
Realizability of advanced control concepts on FPGA hardware
(Third Party Funds Single)
Funding source: Industrie
AI-Supported modeling for friction estimation
(Third Party Funds Single)
Funding source: Industrie
Optimized Reinforcement Architecture for Complex Energy Management
(Third Party Funds Single)
Funding source: Industrie
Development of an innovative camera-based framework for collision-free human-machine movement
(Third Party Funds Single)
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
OXO-LOHC: Autotherme und ultratiefe Wasserstoff-Freisetzung aus LOHC
(Third Party Funds Single)
Funding source: Helmholtz-Gemeinschaft
Model predictive flight control
(Third Party Funds Single)
Funding source: Industrie
Robust energy-based control of MMC/HVDC systems
(Third Party Funds Single)
Funding source: Industrie
Hardware architecture, automatic control, autonomy functionality, and developer community: Modular learning control and planning for mobile professional operation vehicles
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2025
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
Formulation of dispersed systems via (melt) emulsification: Process design, in situ diagnostics and regulation
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2025
Funding source: DFG / Schwerpunktprogramm (SPP)
The aim of this project is the automated production of liquid-liquid disperse systems via melt emulsification, whereby in this process emulsification takes place at elevated temperature. The products obtained after cooling are dispersions of spherical nanoparticles or microparticles. Within the scope of this project, a melt emulsification device for the automated production of product particles with a well-defined particle size distribution (PSD) will be further developed. The PSD has a significant influence on the subsequent product properties, such as flow behavior or drug release kinetics. The PSD of the products is determined by the complex interaction of competing mechanisms. These are, in particular, droplet breakup in a rotor-stator device as a result of shear and elongation stress, as well as coalescence and further ripening, which in turn depend on the system composition, i.e. the emulsifier used (type, concentration) and the dispersion phase (viscosity, volume fraction).
Therefore, for a better process understanding and an active process control, possibilities for in situ determination of the PSD are urgently required. In this project, a novel fiber-coupled measurement system based on broadband elastic light scattering is developed for in situ measurement of the PSD. The system will be validated on reference particle systems and applied to the emulsification process. Furthermore, a hybrid process model is developed, which is the basis for the design of a model predictive control of the process. The model predictive control in combination with the in situ measurement will provide the possibility for an active process control and the production of emulsions with predefined properties and a simultaneous optimization of the process time.
Formulierung von dispersen Systemen durch (Schmelz-)Emulgierung: Prozessgestaltung, In-situ-Diagnostik und Regelung
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2025
Funding source: DFG / Schwerpunktprogramm (SPP)
Ziel dieses Projekts ist die automatisierte Herstellung von flüssig-flüssig-dispersen Systemen über Schmelzeemulgieren, wobei bei diesem Prozess das Emulgieren bei erhöhter Temperatur erfolgt. Als Produkte werden nach der Abkühlung Dispersionen von sphärischen Nano- oder Mikropartikeln erhalten. In Rahmen dieses Projekts wird ein Schmelzeemulgierprozess für die automatisierte Herstellung von Produktpartikeln mit wohldefinierter Partikelgrößenverteilung (PGV) betrachtet. Diese beeinflusst dabei maßgeblich die späteren Produkteigenschaften, wie zum Beispiel das Fließverhalten oder wie die Wirkstofffreisetzungskinetik. Die PGV der Produkte wird dabei durch das komplexe Zusammenspiel konkurrierender Mechanismen bestimmt. Dies sind insbesondere der Tropfenaufbruch in einem Rotor-Stator infolge von Scher- und Dehnbeanspruchung sowie die Koaleszenz und weitere Reifung, die ihrerseits von der Systemzusammensetzung, d.h. dem genutzten Emulgator (Art, Konzentration) und der Dispersphase (Viskosität, Volumenanteil) abhängig sind. Für ein besseres Prozessverständnis und eine aktive Prozessregelung sind daher Möglichkeiten zur in situ Bestimmung der PGV dringend erforderlich. In diesem Projekt wird zur in situ Messung der PGV ein neuartiges, auf breitbandiger elastischer Lichtstreuung basierendes fasergekoppeltes Messsystem entwickelt. Dieses wird an Referenzpartikelsystemen validiert und am Emulgierprozess eingesetzt. Weiterhin wird ein hybrides Prozessmodell entwickelt, das die Basis für das Design einer modellprädiktiven Regelung des Prozesses darstellt. Die modellprädiktive Regelung wird in Kombination mit der in situ Messung die Möglichkeit für eine aktive Prozesssteuerung und die Herstellung von Emulsionen mit vorher definierten Eigenschaften bei gleichzeitiger Optimierung der Prozesszeit ermöglichen.
Predictive and learning control methods
(Third Party Funds Group – Sub project)
Term: 1. November 2022 - 31. October 2025
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
To achieve climate targets, CO2 emissions in the building sector have to be significantly reduced. However, the integration of renewable energy sources increases the complexity of building energy systems and thus the requirements for the operation strategy. Model-based and predictive controllers are necessary for efficient operation. However, due to the high complexity of the energy systems, the development, implementation, and commissioning are very complex leading to high costs, which is why model predictive and optimization-based control strategies are rarely used in practice so far. The goal of the AGENT-2 project is to develop a self-adjusting and self-learning model-predictive control concept that reduces the implementation and commissioning effort and thus increases the applicability of efficient operating strategies in practice. The control concept to be developed is based on distributed agents, each of which learns the system behavior of a subsystem and controls the subsystem. This is based on the findings and the framework developed in the previous project AGENT. The operation of the overall system is achieved by the interaction oft h e self-learning agents with each other. Thus, a self-adjusting and scalable control strategy for building energy systems is created. The self-learning control strategy is compared with state-of-the-art concepts in simulations and tested in practical operation in two demonstration buildings. The findings will be generalized and possibilities for the transfer into practice will be investigated. The project thus contributes to increasing the efficiency of building operation and to reducing the costs of controller implementation and commissioning.
Robust Planning and Control using Probabilistic Methods
(Third Party Funds Group – Sub project)
Term: 1. October 2022 - 30. September 2025
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
Cooperative manipulation with dual-arm robots at the payload limit
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Dual-armrobots offer a high potential for automation technology, as they canbe used to implement tasks that are not possible with one arm alone.This includes in particular the manipulation of large or heavyobjects that exceed the payload of a single arm. Illustrativeexamples are the movement of beverage crates, long boards or pipes,which are also preferably grasped by humans with both hands.
However,cooperative manipulation is particularly challenging, because botharms and the grasped object form a closed kinematic chain. Thecorresponding constraints reduce the number of degrees of freedom andmust be taken into account on the levels of control, trajectory andpath planning. Conversely, the system has the advantage that the loadcan be flexibly distributed on both arms due to the redundantactuators. This is especially crucial for heavy objects, since it isthe only way to comply with actuator torque constraints. The firstgoal of the research project is therefore the development of adynamic load distribution that explicitly takes actuator constraintsinto account and is thus suited for high payloads. To this end, anoptimization-based approach is pursued with a focus on efficiency andreal-time capability.
Moreover,this load distribution must be taken into account on all systemlevels, since otherwise large payloads can lead to the situation thatno admissible trajectory can be computed for a path or that atrajectory is not executable by the controller. Consequently, thesecond goal is the consistent consideration of the dynamic loaddistribution. On the control level, this includes not only theisolated solution of the optimal load distribution in each samplingstep, but also the approach of a forward-looking model predictivecontroller. For trajectory planning, on the one hand, a time-optimaltrajectory generation with subordinate solution of the dynamic loaddistribution and, on the other hand, the extension of the modelpredictive controller to a predictive path-following controller shallbe investigated. Furthermore, a path planner for dual-arm robots willbe developed for the first time, which explicitly considers thepayload and can be extended in a modular manner to take collisions aswell as the additional degrees of freedom of a mobile base intoaccount.
Thethird goal is the extensive experimental validation of the control,trajectory and path planning methods in order to practicallydemonstrate the potential of cooperative manipulation with dual-armrobots. For this purpose, a mobile dual-arm robot with additionalmotion capturing system from the DFG major instrumentation proposal438833210 is available at the Chair of Automatic Control. Inparticular, movements with large and heavy objects shall beperformed, whose mass is in the order of magnitude of the combinedmaximum payload of both arms.
2025
2024
2023
2022
2021
2020
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