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Neural force control (NFC) for complex manipulator tasks

  • Part V: Robotics, Adaptive Autonomous Agents, and Control
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

In order to apply intelligent robot control to complex force/position tasks, we developed a novel concept for force/position control based on neural networks (NN). A neural dynamic net (NDN) contains a neural computed torque controller, which delivers a precise mapping of the inverse model of the real manipulator. Specifically, the mapping considers different types of nonlinear properties, which are essential for this type of control, but are hard to model analytically. Furthermore the inverse kinematics is represented by a neural kinematics network (NKN), which includes strategies for avoidance of singularities, self-collisions, and conflicts with workspace constraints. This neural control approach has been tested in simulations and will be applied to a 6 DOF industrial manipulator to various demanding tasks including screw removal and surface tracking with constant normal force.

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Author information

Authors and Affiliations

  1. Dept. of Comp. Science VI, University of Bonn, Römerstr. 164, D-53117, Bonn, Germany

    M. Dapper, R. Maa\, V. Zahn & R. Eckmiller

Authors
  1. M. Dapper
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  2. R. Maa\
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  3. V. Zahn
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  4. R. Eckmiller
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Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Dapper, M., Maa\, R., Zahn, V., Eckmiller, R. (1997). Neural force control (NFC) for complex manipulator tasks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020250

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  • DOI: https://doi.org/10.1007/BFb0020250

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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