FEATURES

Today, the integration of robotic arms in existing processes is a time consuming, complex and expensive task. This also aco-pplies for the adjustment of the behavior of robotic arms with changing production procecces. Especially for small and medium size companies with lower production quantities and more frequent changes in the production process, this is not feasible. Even more complex is the design of robotic behavior in human co-working scenarios. Designing repetitive tasks, that are executed regardless of the personal properties and requirements of the human co-worker is leading to poor acceptance.

In this project we use wearable devices (smart clothing) to track human body motions and let industrial robot arm mimic them. In this way automation an co-working task are designes in a fast, efficient manner. Our main objective is to improve the implementation and execution of control solutions for robotic arms in working scenarios. More concrete, we want to: - Enable easy, fast, inexpensive and reliable teaching of tasks of robotic arms at the production site for co-working scenarios - Enable personalized control of the robotic arm, depending of the properties of the co-worker - Enable runtime adaptation of the control software based on dynamic environmental properties (e.g., concrete position of the co-worker)

WEARABLES

The working clothes, both jacket and glove are equippedwith several IMIs: BNO055 is an intelligent 9-axis absolute orientation sensor.

SOPHISTICATED KINEMATICS

The online manipulation of the robot arm is bases on tracking the human body motion.Therefore, the data from the IMUs is used to determine the posture of the human arm using forward kinematics.

FOG COMPUTING

Industry 4.0 demands more flexible, secure and reliable infrastructure. WEIR is based on a modern Fog Computing Plug-and-Play architecture, enabling edge-computing and seamless integration of new components.

MOBILE APP

A mobile App is used by the trainer to define workflows and triggers for stepping from one control step to another.

About

Awesome Robot Software Group

The team consists of 5 members of the Software Technology Group of the Technische Universität Dresden under the supervision of Prof. Dr. rer. nat. habil. Uwe Aßmann. The ST group is one of the most recognized software engineering institutions in the research areas of Software Composition, Model-Driven and Component-Based Software Engineering. The group has a strong background in the areas of meta-modeling, software architectures, self adaptive systems and language engineering. In recent years the ST Group has used multiple different robotic hardware systems to demonstrate and evaluate contributions in the area of the design and execution of self-adaptive systems. Recently, the group also investigates software design concepts for electronic components embedded in clothes (wearable computing).

  • SOFTWARE ARCHITECTURE
  • SELF ADAPTIVE SYSTEMS
  • META-MODELLING
  • TESTING

UBIANCE TEAM MEMBERS

The team consists of 5 members of the Software Technology Group of the Technische Universität Dresden.
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CHRISTIAN PIECHNICK

self-adaptive software systems
Christian is a researcher in the field of self-adaptive software systems and engineer of the Smart Application Grids platform for developing and executing self-adaptive software.
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GEORG PÜSCHEL

model-driven testing
Georg is doing research in the area of model-driven testing using model-driven techniques to test self-adaptive systems in open environments (e.g., mobile robots)
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MARIA PIECHNICK

wearable devices
Maria is doing her PhD Thesis on plug-and-play software/hardware concepts for wearable devices (i.e., smart clothes)
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SEBASTIAN WERNER

robotic platforms
Sebastian is a researcher in the area of self-adaptive algotrithms for robotic platforms.
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JAN FALKENBERG

cyber-physical systems
Jan is a researcher in the area of modular and compositional Cyber-Physical Systems

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