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Thread: Miscellaneous

  1. #11


    What is Industrial Automation?

    Published on Aug 7, 2015

    This video is a very simple introduction into the world of industrial automation.

  2. #12


    Dynamic Compensation - Toward the Next-Generation Industrial Robot

    Published on Nov 24, 2015

    Traditionally it is difficult for industrial robots to achieve high-speed motion with high accuracy due to large dynamical uncertainties. We present a solution using dynamic compensation by adopting high-speed vision and actuators to compensate for the uncertainties caused by robot system itself as well as external environment. Here, we present two typical tasks - fast and accurate contour-tracking and high-speed peg-and-hole alignment, with a commercial industrial robot. Traditionally, the playback method is the most common approach to control an industrial robot. However, it is time-consuming and exhausting to teach an accurate path point by point. We propose to perform these tasks by adding a high-speed robotic module under the dynamic compensation scheme. Through this method, a coarse global path can be easily taught with very few roughly chosen teaching points. The errors between the coarse path and the target path are then dynamically compensated by the high-speed robotic module under 1,000 fps visual feedback. As a result, accurate tracking as well as peg-and-hole alignment can be achieved with fast speed.
    This technology can improve existing industrial robots’ performance while at the same time reduce the workload of robot operators. It may find applications in many industrial tasks, such as in welding, painting as well as assembly. This system will be demonstrated on the coming iRex 2015(International Robot Exhibition 2015) in Tokyo from Dec.2 to Dec.5 at Tokyo Big Sight. You are welcome to visit our booth.

    k2.t.u-tokyo.ac.jp/fusion/DCmethod

  3. #13


    Collaborative Robots at IREX 2015

    Uploaded on Dec 3, 2015

    "New collaborative robots at IREX 2015"

    by Samuel Bouchard
    December 4, 2015

  4. #14

  5. #15


    Differential feed control applied to corner matching in automated sewing

    Uploaded on May 20, 2016

    “Differential Feed Control Applied to Corner Matching in Automated Sewing,” by Johannes Schrimpf and Geir Mathisen from Norwegian University of Science and Technology, SINTEF Raufoss Manufacturing, and Applied Cybernetics, Norway. Presented at ICRA 2016.

  6. #16

  7. #17


    Jller – Prokop Bartonicek & Benjamin Maus
    May 18, 2016

    Jller is part of an ongoing research project in the fields of industrial automation and historical geology. It is an apparatus, that sorts pebbles from a specific river by their geologic age. The stones were taken from the stream bed of the German river Jller, shortly before it merges with the Danube, close to the city of Ulm. The machine and its performance is the first manifestation of this research.
    A set of pebbles from the Jller are placed on the 2x4 meter platform of the machine, which automatically analyzes the stones in order to then sort them. The sorting process happens in two steps: Intermediate, pre-sorted patterns are formed first, to make space for the final, ordered alignment of stones, defined by type and age. Starting from an arbitrary set of stones, this process renders the inherent history of the river visible.
    The history, origin and path from each stone found in a river is specific to the location, as every river has a different composition of rock types. The origin of those stones is well documented. For instance, the ones from the river Jller derive from two origins. Some come from rocks, that are the result of erosions in the Alps and are carried in from smaller rivers. Other stones have been ground and transported by glaciers that either still exist, or existed in the ice ages. As the Alps and flats, that were once covered by glaciers, have shifted, even deeper rock-layers were moved and as a result, stones from many geologic periods make their way into a river.
    When the history of a river is known, the type of stone can be directly related to its geological age. One very common sedimentary rock is the dark grey limestone from the Triassic period (225 million years ago). It was formed from the layers of sediments in the primeval ocean. Granodiorite, on the other hand, is an igneous rock of volcanic origin from the Tertiary Period (30 to 40 million years ago). Between those types there is a variety of metamorphic rocks, created by the transformation of existing rock types through the influence of temperature and pressure over time. Furthermore, a small amount of pebbles are formed by non-rock materials like red brick or slag, that have their origin in the Anthropocene.
    Most of the time, stones do not appear as a singular uniform material, but as a composition of different, laminated or layered materials. A prominent example of his are the white lines of lime in grey pebbles.
    Jller was presented as part of Ignorance, a collaborative exhibition of German artist Benjamin Maus and Czech artist Prokop Bartonicek.
    Technology: The machine works with a computer vision system that processes the images of the stones and maps each of its location on the platform throughout the ordering process. The information extracted from each stone are dominant color, color composition, and histograms of structural features such as lines, layers, patterns, grain, and surface texture. This data is used to assign the stones into predefined categories. Those categories represent the range of stones that can be found in the specific river and correspond directly to the age of the stone. They are the result of a classification system that is trained by sets of manually selected and labeled stones. Because there are only a limited number of stone types that can be found in a specific river, this system proves to be very accurate.
    The stones get picked up by an industrial vacuum gripper, which can rotate around its own axis. This way the pebbles can also be aligned.
    Prokop Bartonicek (CZ) – Born in Prague in 1983. In the year 2003 he was accepted into the atelier of sculpture of prof. Beranek at UMPRUM. Between the years 2007-2008 he was a student of prof. Joachim Sauter at UdK Berlin. Presently, he lives and works in Prague, primarily focuses on developing light, interactive and experimental projects such as Vibrator or Mirrsaic.
    2008-2015 he organised art exhibitions in order to present the Berlin experimental scene in Prague. Furthermore, he has founded the cultural centre Ex Post.
    Benjamin Maus (DE) – is self taught in many disciplines and started taking apart apparatuses and learning programming early in his life. In 2011 he founded the studio FELD. His projects have been exhibited and awarded internationally multiple times. He has a broad knowledge in many fields like mechanics, physics and computer science. His interested in different modes of production - especially industrial automation - and their impact on society. This is the foundation for his work: Machines that perform seemingly meaningful tasks.
    Credits:
    Authors, Concept: B. Maus (DE) P. Bartonicek (CZ)
    Production: P. Bartonicek
    Mechanics: B. Maus, P. Bartonicek, T. Arnaudov (CZ)
    Electronics: B. Maus, P. Rusnak (CZ), T. Arnaudov
    Programming: B. Maus, P. Rusnak
    Video: T. Posselt (DE)
    Jller (Ignorance, with Benjamin Maus), 2015

    "Someone built a rock-sorting robot and it is downright hypnotizing"

    by Liz Stinson
    May 24, 2016

  8. #18

  9. #19


    How AI can bring on a second Industrial Revolution | Kevin Kelly

    Published on Jan 12, 2017

    "The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly -- and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."

  10. #20


    Bin picking example

    Published on Mar 28, 2017

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