Lehrstuhl für Mechatronik in Maschinenbau und Fahrzeugtechnik (MEC)


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Demand-Side- und Produktions-Management für Getränkeabfüllprozesse (DESPRIMA)

DESPRIMA addresses modeling and control tasks in energy efficient DSM based production. In particular, we develop, validate and practically implement hierarchical and distributed planning and control strategies of production and energy management for both, individual and coupled production lines. To this end, nonlinear dynamic models for beverage filling machines and production lines shall be first developed. The resulting holistic models describe the dynamics of the production machines, the impact of the power flexibility potential, as well as the DSM system services and energy consumption constraints. The DSM-based energy-efficient planning and control of the individual and coupled production lines adapt in real time to changes in the environment, such as energy consumption, production requirements, weather conditions, stock market and process disruptions. The control algorithms comprise a broad design framework covering MPC, energy-efficient hierarchical production and a hybrid dynamical setup.

Funded by: Bundesministerium für Wirtschaft und Energie
Time span: July 2019 − June 2022

Hochautomatisiertes und straßenschonendes Fahren auf Basis der Bodenradarsignale (RADSPOT)

This project focuses on the development of innovative AI-based autonomous driving algorithms boosted by ontological and knowledge graph models living in a Digital Twin (DT). To this end, a framework of hierarchical reinforcement learning, consisting of a multi-layer decision policy is  applied. The learning agent(s) is (are) then able to choose not only elementary actions, but also to learn how to combine missions at a higher abstraction level. On the other hand, the DT maps the physics of the entire traffic on a road segment and, in its current development stage, serves as a cloud-based predictive maintenance of the road infra- and substructure. E.g. novel road-preserving autonomous driving can be implemented, by taking into account the inferred damage spots during a real-time path planning.

Funded by: Bundesministerium für Verkehr und digitale Infrastruktur
Time span: October 2018 − December 2022

Resilienz in Mixed-Criticality-Systemen des Industriellen Internet der Dinge (ReMiX)

In the ReMiX project, a design methodology for verifiable system architectures in intelligent automation is to be developed. For this purpose, distributed resources are summarized as a shared virtual resource and organized according to the principles of mixed-criticality systems. Mixed-Criticality describes a mapping of functions to resources based on their criticality according to available resource quotas. The distributed resources are merged as a shared virtual resource and organized according to the principles of systems with different criticality. The research results of this project will contribute to increase the system resilience through new design methods for self-organizing communication, computing and control approaches. By integrating security aspects into the design methodology, we aim to extend our development framework to attack-resistant mixed-criticality systems.

Funded by: Bundesministerium für Bildung und Forschung
Time span: September 2019August 2022

Multifunktionale mobile Roboterplattform für ein digitales Produktionsfeld der additiven Fertigung (KIMKO)

The aim of KIMKO is to develop a robot system consisting of a mobile platform, two lightweight robots and stereo cameras for use in a 3D printing farm. The main research topic within this project is the collisionfree online trajectory planning for the manipulators and the mobile platform as well as their coordination in order to navigate autonomosly and to cooperatively plan and perform the robot motions/tasks in a confined place. Due to the high structural flexibility, model-based predictive control strategies are used for trajectories generation, whereas AI-based machine vision methods are beeing considered for the environment perception and online map generation (SLAM).

Funded by: AiF-Projekt GmbHG, BMWi
Time span: August 2019 − December 2021

Kollaborative Roboter-Roboter-Mensch Interaktion beim Fruchtauflegen (CooPick)

CooPick develops a flexible and scalable robot system, which can be integrated into existing manufacturing processes and is able to perform task support in a cooperative robot-robot and robot-human interaction. Several lightweight robots communicate with each other and coordinate their actions and missions. Therefor, centralised and distributive collision-free model predictive control (MPC) algorithms combined in a hierarchy with scheduling have been developed. In a use-case a fruit-sorting task has been considered, where additionally an interplay with human operators is addressed. 

Funded by: AiF-Projekt GmbHG, BMWi
Time span: January 2018 − June 2020

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