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Den in der industriellen Produktion eingesetzten Manipulatoren fehlt in der Regel die Möglichkeit, ihre Umwelt wahrzunehmen. Damit Mensch und Roboter in einem gemeinsamen Arbeitsraum arbeiten können, wird im SIMERO-System die Transferbewegung des Roboters durch Kameras abgesichert. Dieses Kamerasystem wird auf Ausfall überprüft. Dabei werden Fehler in der Bildübertragung und Positionierungsfehler der Kameras betrachtet.
In many robotic applications, the teaching of points in space is necessary to register the robot coordinate system with the one of the application. Robot-human interaction is awkward and dangerous for the human because of the possibly large size and power of the robot, so robot movements must be predictable and natural. We present a novel hybrid control algorithm which provides the needed precision in small scale movements while allowing for fast and intuitive large scale translations.
Zur Zeit haben Industrieroboter nur eine sehr begrenzte Wahrnehmung ihrer Umwelt. Wenn sich Menschen im Arbeitsraum des Roboters aufhalten sind sie daher gefährdet. Durch eine Einteilung der möglichen Roboterbewegung in verschiedene Klassen kann gezeigt werden, dass die für einen Menschen im Arbeitsraum gefährlichste Bewegung die freie Transferbewegung ist. Daher besteht die betrachtete Aufgabe darin, diese Transferbewegung eines Manipulators durchzuführen, ohne mit dynamischen Hindernissen, wie zum Beispiel Menschen, zu kollidieren. Das SIMERO-System gliedert sich in die vier Hauptkomponenten Bildverarbeitung, Robotermodellierung, Kollisionserkennung und Bahnplanung. Diese Komponenten werden im einzelnen vorgestellt. Die Leistungsfähigkeit des Systems und die weiteren Verbesserungen werden an einem Versuch exemplarisch gezeigt.
This paper analyzes the problem of sensor-based colli-sion detection for an industrial robotic manipulator. A method to perform collision tests based on images taken from several stationary cameras in the work cell is pre-sented. The collision test works entirely based on the im-ages, and does not construct a representation of the Carte-sian space. It is shown how to perform a collision test for all possible robot configurations using only a single set of images taken simultaneously.
It is difficult for robots to handle a vibrating deformable object. Even for human beings it is a high-risk operation to, for example, insert a vibrating linear object into a small hole. However, fast manipulation using a robot arm is not just a dream; it may be achieved if some important features of the vibration are detected online. In this paper, we present an approach for fast manipulation using a force/torque sensor mounted on the robot's wrist. Template matching method is employed to recognize the vibrational phase of the deformable objects. Therefore, a fast manipulation can be performed with a high success rate, even if there is acute vibration. Experiments inserting a deformable object into a hole are conducted to test the presented method. Results demonstrate that the presented sensor-based online fast manipulation is feasible.
Virtual Robot Programming for Deformable Linear Objects: System concept and Prototype Implementation
(2002)
In this paper we present a method and system for robot programming using virtual reality techniques. The proposed method allows intuitive teaching of a manipulation task with haptic feedback in a graphical simulation system. Based on earlier work, our system allows even an operator who lacks specialized knowledge of robotics to automatically generate a robust sensor-based robot program that is ready to execute on different robots, merely by demonstrating the task in virtual reality.
This paper deals with the handling of deformable linear objects (DLOs), such as hoses, wires or leaf springs. It investigates the a priori knowledge about the 6-dimensional force/torque signal for a changing contact situation between a DLO and a rigid polyhedral obstacle. The result is a complete list, containing for each contact change the most significant combination of force/torque signal components together with a description of the expected signal curve. This knowledge enables the reliable detection of changes in the DLO contact situation and with it the implementation of sensor-based manipulation skills for all possible contact changes.
We present a system concept allowing humans to work safely in the same environment as a robot manipulator. Several cameras survey the common workspace. A look-up-table-based fusion algorithm is used to back-project directly from the image spaces of the cameras to the manipulator?s con-figuration space. In the look-up-tables both, the camera calibration and the robot geometry are implicitly encoded. For experiments, a conven-tional 6 axis industrial manipulator is used. The work space is surveyed by four grayscale cameras. Due to the limits of present robot controllers, the computationally expensive parts of the system are executed on a server PC that communicates with the robot controller via Ethernet.
Diese Arbeit skizziert einen allgemeinen Ansatz zur Montage deformierbarer linearer Werkstücke (wie Kabel, Drähte, Schläuche, Blattfedern) mit Industrierobotern. Hierzu werden insbesondere die folgenden zwei Aspekte betrachtetet. Erstens die zuverlässige Ausführung der Montage unter Berücksichtigung der Werkstückdeformation und anderer Unsicherheiten, zweitens die numerische Simulation des Werkstückverhaltens. Zur robusten Ausführung der Montage wird das aus der Montage starrer Werkstücke bekannte Konzept der Manipulation-Skills auf deformierbare Werkstücke übertragen. Bei der numerischen Simulation wird insbesondere die Bestimmung der Greifertrajektorie bei gegebener Aufgabenstellung betrachtet.
Manipulating deformable linear objects - Vision-based recognition of contact state transitions -
(1999)
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presented which allow the state recognition of the object while being manipulated by the robot.