Advertisement

Combining Visual, Tactile and Range Sensor Information to Increase the Autonomy of Robots

  • M. Seitz
  • A. Weigl
  • K. Hohm
  • K. Kleinmann
Chapter
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 18)

Abstract

Up to now, most industrial robots employ sensors just to measure their internal state and to keep their end-effector on a desired trajectory. However, sensors monitoring contact situations and/or giving information about the environment can enhance the capabilities of robot manipulators. Reasons, why they are not used in general are additional costs, sceptism about their reliability, limitations in the robot controllers and computational burden. But cost reductions in sensor hardware as well as the development of industrial interfaces to Personal Computers with their rich environment of easily handable software has changed this picture. The exploitation of the abilities of further sensors has opened a large field of new robot applications in industry — e.g. robot assisted disassembly — as well as in direct human support, the broad field of so-called service robotics.

Keywords

Robot System Tactile Sensor Object Manipulation Gray Level Image Touch Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Haralick, R., and Shapiro, L.: Computer and Robot Vision, Vol. II, Addison Wesley Pub., 1993.Google Scholar
  2. 2.
    Horn, B.: Robot Vision, The MIT Press, Cambridge, USA, 1986.Google Scholar
  3. 3.
    Shirai, Y.: 3D Computer Vision, MIT Press, Cambridge, 1987.Google Scholar
  4. 4.
    Joseph, R. and Rowland, J.: Fusing Diverse Sensor Data By Processing Abstract Images, Proc. Int. Conf. Intelligent Autonomous Systems, Karlsruhe, Germany, 1995.Google Scholar
  5. 5.
    Trobina, M., Leonardis, A. and Ade, F.: Grasping Arbitrarily Shaped Objects, DAGM Symp. Mustererkennung, Vienna, Austria, 1994.Google Scholar
  6. 6.
    Howe, H.: Tactile Sensing and Control of Robotic Manipulation, Advanced Robotics, Vol. 8, No. 3, 1994.Google Scholar
  7. 7.
    Seekircher, J. and Hoffmann, B.: Improved Tactile Sensors, Int. Symp. Robot Control, Karlsruhe, Germany, 1988.Google Scholar
  8. 8.
    Holweg, E.: Autonomous Control in Dexterous Gripping, Phd Thesis, Delft University of Technology, 1996.Google Scholar
  9. 9.
    Allen, P.: Robotic Object Recognition Using Vision and Touch, Kluwer Academic, Norwell, USA, 1987.CrossRefGoogle Scholar
  10. 10.
    Maekawa, H., Tanie, K., and Komoriya, K.: Tactile Sensor-Based Manipulation of an Unknown Object by a Multifingered Hand with Rolling Contact, Proc. Int. Conf. Robotics and Automation, Nagoya, Japan, 1995.Google Scholar
  11. 11.
    Vischer, D.: Cooperating Robot with Visual and Taktile Skills, Proc. Int. Conf. Robotics and Automation, Nice, Prance, 1992.Google Scholar
  12. 12.
    Bard, C., Laugier, C. and Milesi-Bellier, C.: An Integrated Approach to Achieve Dexterous Grasping from Task-Level Specification, Proc. Int. Conf. Intelligent Robots and Systems, Munich, Germany, 1994.Google Scholar
  13. 13.
    Paetsch, W. and Kaneko, M.: A Three Fingered, Multijointed Gripper for Experimental Use, Proc. Int. Workshop Intelligent Robots and Systems, Tsuchiura, Japan, 1990.Google Scholar
  14. 14.
    Corke, P.: Visual Control of Robot Manipulators — A Review, Visual Servoing, K. Hashimoto (Ed.), World Scientific Series in Robotics and Automation — Vol. 7, 1993.Google Scholar
  15. 15.
    Yoshimi, B. and Allen, P.: Active, Uncalibrated Visual Servoing, Proc. Int. Conf. Robotics and Automation, San Diego, USA, 1994.Google Scholar
  16. 16.
    Seitz, M., Holeschak, U. and Kleinmann, K.: Active Inspection and Handling of Unknown Objects Using an Autonomous Hand-Arm-Eye System, Proc. Int. Conf. Intelligent Autonomous Systems, Karlsruhe, 1995.Google Scholar
  17. 17.
    Weigl, A. and Seitz, M.: Vision Assisted Disassembly Using a Dexterous Hand-Arm System: An Example and Experimental Results, Proc. Int. Symp. Robot Control, Capri, Italy, 1994.Google Scholar
  18. 18.
    Weigl, A., Hohm, K. and Seitz, M.: Processing Sensor Images for Grasping Disassembly Objects with a Parallel-Jaw Gripper, Proc. TELEMAN Telerobotics Research Conference & ERNET Workshop, Noordwijkerhout, The Netherlands, 1995.Google Scholar
  19. 19.
    Weigl, A., Hohm, K. and Tolle, H.: A Flexible Tactile Grasping Strategy for Autonomous Robotic Disassembly, Proc. Int. Symp. on Industrial Robots, Milano, Italy, 1996.Google Scholar
  20. 20.
    Holweg, E., Hoeve, H., Jongkind, W., Marconi, L., Melchiorri, C. and Bonivento, C.: Slip Detection by Tactile Sensors: Algorithms and Experimental Results, Proc. Int. Conf. Robotics and Automation, Minneapolis, USA, 1996.Google Scholar
  21. 21.
    Marconi, L. and Melchiorri, C.: Incipient Slip Detection and Control Using a Rubber-Based Tactile Sensor, IFAC World Congress, San Francisco, USA, 1996.Google Scholar
  22. 22.
    Howe, H. and Cutkosky, M.: Sensing Skin Acceleration for Slip and Texture Perception, Proc. Int. Conf. Robotics and Automation, Scottsdale, Arizona, USA, 1989.Google Scholar
  23. 23.
    Tremblay, M. and Cutkosky, M.: Estimating Friction Using Incipient Slip Sensing During a Manipulation Task, Proc. Int. Conf. Robotics and Automation, Atlanta, Georgia, USA, 1993.Google Scholar
  24. 24.
    Mingrino, A., Bucci, A., Magni, R. and Dario, P.: Slippage Control in Hand Prostheses by Sensing Grasping Forces and Sliding Motion, Proc. Int. Conf. Intelligent Robots and Systems, Munich, Germany, 1994.Google Scholar
  25. 25.
    Mason, M. and Salisbury, J.: Robot Hands and the Mechanics of Manipulation, MIT-Press, Camebridge, USA, 1985.Google Scholar
  26. 26.
    Buss, M. and Kleinmann, K.: Multi-Fingered Grasping Experiments Using Real-Time Grasping Force Optimization, Int. Conf. Robotics & Automation, ICRA′96, Minneapolis, USA, 1996.Google Scholar
  27. 27.
    Kleinmann, K., Bettenhausen, D. and Seitz, M.: A Modular Approach for Solving the Peg-In-Hole Problem with a Multifingered Gripper, Proc. Int. Conf. on Robotics and Automation, Nagoya, Japan, 1995.Google Scholar
  28. 28.
    Weigl, A., Schwartz, M. and Bettenhausen, K.: A Flexible Motion Control Structure for Autonomous Robotic Disassembly, Proc. Int. Conf. CAD/CAM Robotics and Factories of the Future (Gill, R. and Syan, C.S., Ed.), Middlesex University Press, London, UK, 1996.Google Scholar
  29. 29.
    Seitz, M.: Untersuchungen zur Nutzung von Bildverarbeitung für Manipulations aufgaben in der Robotik, Phd Thesis, Darmstadt University of Technology, Shaker Verlag Aachen, 1996.Google Scholar
  30. 30.
    Weigl, A.: Exemplarische Untersuchungen zur flexiblen automatisierten Demontage elektronischer Geräte mit Industrierobotern, Phd Thesis, Darmstadt University of Technology, Shaker Verlag Aachen, 1997.Google Scholar
  31. 31.
    Kleinmann, K.: Lernende Regelung eines Mehrfingergreifers, Phd Thesis, Darmstadt University of Technology, 1996.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • M. Seitz
    • 1
  • A. Weigl
    • 1
  • K. Hohm
    • 1
  • K. Kleinmann
    • 1
  1. 1.Control Systems Theory and Robotics LaboratoryDarmstadt University of TechnologyDarmstadtGermany

Personalised recommendations