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    Fibigerstræde, 16

    9220 Aalborg Øst


Organisation profile

Organisation profile

At the Department of Mechanical and Manufacturing Engineering, Aalborg University a group of researchers is focused on research in adaptive automation of manufacturing tasks. An adaptive automatic manufacturing system is a system which automatically or semi-automatically can respond to changes in the operating environment.

The mission of the research group is: to create, discover and disseminate knowledge about adaptive automation through research, education, prototype developments and practical implementation.

The vision of the research group is to, within five years, establish frontier research and education facilities within adaptive automation which is ranked among one of the best in Europe.


The environment in which most industrial companies are operating is steadily becoming more and more dynamic due to many different conditions (e.g. increased customisation, Smaller and smaller batch sizes, Increased global competition focus on sustainability, technology development).

As a result of this, manufacturing system must be able to cope with an increasing number of variants, to react fast to marked changes, and to have an increased ability to handle the increasing uncertainty associated with the market (market size, competitors, capacity needs, etc).

Existing automatic manufacturing systems are not sufficiently flexible to meet these demands. Normally, they have been developed with a focus on reducing standard unit costs, and improving product quality and ergonomics. These are still very important issues; however, the automatic manufacturing systems of the future must be more adaptive so that they can respond fast and efficient to changes in the operating environment.

It is the development and application of these adaptive systems which is the focus of our research group.


The competences of the research group are related to advanced automation of manufacturing. Areas covered by the research group are:

  • Industrial automation
  • robotics
  • computer vision
  • machine learning
  • modelling and control of industrial processes
  • sensor based control
  • design of advanced manufacturing systems
  • digital manufacturing.


The formulated vision is very ambitious and requires research in a large number of areas found in the cross-road between established manufacturing, robotics and automation technologies and emerging technologies in industrial ICT, A.I. cognition, mobile and service robots, autonomous control, and sensing. The huge area cannot be covered by the research group. Instead focus is on a number of selected research objectives and on establishing close collaboration with external partners nationally as well as internationally.

We have selected to focus on:

1. Automation - Making the development process of automatic manufacturing systems more efficient so that:

  • systems can be developed faster
  • cheaper systems can be developed
  •  the development can be integrated into the product innovation process.

2. Scalability - Developing systems which fast and efficient can be scaled (in terms of capacity) to meet market demands.

3. Cost/diversity - Making automatic manufacturing systems flexible as well as efficient (goal: standard unit cost independent of volume).

Our Work in Practice

We prioritise that our research is put into practical use. As a result of this most of our research is carried out in close collaboration with industry. This can be end-users and well as system and technology providers.

Furthermore, since it is a characteristic of automation activities in manufacturing that good solutions requires a multi disciplinary approach, we have a great emphasis on establishing close collaboration with research institutions nationally as well as internationally.

We build prototypes (theory guides – but experiments decides)

Finally, we have a focus on maintaining state-of-the-art practical competences in areas as: digital manufacturing, machine vision, Robotics, analysis of manufacturing.


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Collaborations from the last five years

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