Job Offer.


 

In the framework of a cooperation project between the university of Freiburg, institute of image processing and pattern recognition and the company INOS GmbH, a real-time  intelligent robotic system is being developed, according to which objects of various kinds and shapes, (mainly card-board boxes and sacks) piled on a platform, should be recognized, grasped, unloaded and then neatly placed to a target platform. In some cases the dimensions of the target objects should be calculated a well. We expect to promote the system to the automobile and cargo distribution industry. Some examples of the objects which we want to be able to recognize, along with  the configurations frequently encountered at target pallets, are depicted in figure 1.

 

 

 

Figure 1: Target Object Configurations

 

 

In order to solve this problem, we use an industrial robot from KUKA GmbH, as well as a SICK LMS200 laser sensor attached on the hand of the robot. Note that the sensor is placed on the forefront of a gripper, based on vacuum principles. The robotic system is depicted in figure 4.

 

 

Figure 2:  The Robotic System

 

 

 Placing the sensor on the hand of the robot, has the advantage that scene data can be efficiently acquired. The usage of the laser sensor as a primary data acquisition tool, renders the system independent of the environmental conditions. This is very important, since at the future installation sites, the quality of environmental conditions are adverse. Cameras will be used in a secondary manner.

According to the concept described above, we have already implemented an initial system, which manages to unload card board boxes, which are neatly placed on target platforms as in figure 4.  We have employed an efficient edge detection algorithm for this purpose, which detects boxes’ corners from the images of the laser sensor  and assumes the boxes position in space. The figure 5a-d illustrates the system’s operation.

     

 

Figure 3: The robotic System in Action

We want now to complete the construction of a system which manages to deal with arbitrary configurations of  objects. In this respect, we move the robot’s hand parallel to the objects, having the sensor pointing to the ground, so as an image of the upper surface of the objects to be captured. For the configuration of figure 1, the acquired image is depicted in figure 4.

 

Figure 4: Target Object Configuration

Figure 5: Edge map

 

 

Finally, feature extractors are applied to the image in order to extract characteristics which will allow for the formation of an hypothesis of the location of the objects in the scene. For the card - board boxes case, such features could be the boxes' vertices, which can be relatively easily extracted from the edge map of figure 5.

 

We are looking for two students who will help on the implementation and experimental evaluation of this robotic system, either as assistants with payment, or pursuing a diploma thesis. The candidates will have the opportunity to learn the essentials of developing a state of the art industrial system, as well as dealing with industrial robots. The candidates should be familiar with basic knowledge of image processing and capable of programming in C or C++. The knowledge of written and spoken English is necessary. But what is most important is imagination and desire for creation! If you feel like having the above qualifications then email us as soon as possible:

 

 

Corresponding person:

Dipl.-Ing. Dimitrios Katsoulas

Albert-Ludwigs-Universität Freiburg

Lehrstuhl für Mustererkennung und Bildverarbeitung.

Email: dkats@informatik.uni-freiburg.de

URL:   http://lmb.informatik.uni-freiburg.de/people/dkats

 


 

Stellenangebote.

 

Zwei Stellen für Diplom / Student Arbeiten oder Wissenschaftliche Hilfskräfte, sind ab sofort zu besetzen. Was wir machen ist ein Kooperationsprojekt zwischen der Firma INOS Automationssoftware  GmbH und des Lehrstuhles für Mustererkennung und Bildverarbeitung. Ziel des Projektes ist die  Entwicklung eines modularen Systems mit dreidimensionaler Lage und Positionserkennung  von unterschiedlichen, allgemeinen Objektformen zum intelligenten, roboterunterstützten Depalletieren/Palletieren. Dass heißt dass wir beim Programmieren eines Industriellen Roboter, einzelne Objekte wie z.B. Boxen / Kisten, Flachbehälter und Säcke auf einer Palette lageunabhängig zu erkennen, zu greifen und an anderer Stelle wieder abzulegen versuchen. Die Ausweitung der zu greifenden Objekte auf Säcke als Vertreter von Objekten mit variabler Struktur stellt einen zusätzlichen Schwierigkeitsgrad dar. Mit dem neu zu entwickelnden Erkennungssystem soll es möglich sein, ein Objekt in Echtzeit zu erkennen und zu greifen. Dieser Vorgang soll von dem System in einer Zeitspanne bewerkstelligt werden, die auch ein Mensch für die selbe Tätigkeit benötigt.

 

Adressaten:

Studenten der Informatik, Physik oder Mathematik mit ausreichenden Kenntnisse in C, C++.

 

 

Bei Interesse erhalten Sie weitere Informationen bei:

Dipl.-Ing. Dimitrios Katsoulas

Albert-Ludwigs-Universität Freiburg

Lehrstuhl für Mustererkennung und Bildverarbeitung.

Email: dkats@informatik.uni-freiburg.de

URL:   http://lmb.informatik.uni-freiburg.de/people/dkats