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Part of the book series: Studies in Cognitive Systems ((COGS,volume 26))

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Abstract

Contrary to feedforward networks, networks with recurrent connections show dynamic, in the sense of time-dependent properties. Therefore, recurrent networks are well suited to control behavior varying in time and, in this way, are particularly interesting for the solution of motor control tasks in both robots and animals. However, there is only a small theoretical basis concerning the properties of massively parallel recurrent systems. Only a few special types, in particular the Hopfield network, have been investigated in some detail. Here we describe another type of recurrent network that shows interesting properties and that can be applied to solve a basic problem in motor control. To control a multijoint manipulator with revolute joints, the actuators have to control joint angles. Thus, the controller has to provide signals giving angle values, i. e. , it has to operate in joint space coordinates. The task of the manipulator, however, is usually given in another coordinate system, namely in workspace coordinates. In many cases, the workspace coordinates are provided by a visual system. These workspace coordinates could, for example, be given as Cartesian coordinates. Therefore, the controller has to cope with the problem of transformation between workspace and joint space coordinates. Another problem is that the position of the manipulator may be subject to external disturbances. The classical way to solve this problem is to apply negative feedback control. In the feedback loop, the joint space values will therefore be transformed to workspace coordinates. This is called direct transformation. In the feedforward loop, the workspace coordinates have to be transformed into joint space coordinates, which is called inverse transformation. Here, we will concentrate only on kinematic systems. In this case, the transformations are called direct kinematics and inverse kinematics, respectively. If we deal with a redundant system, for example, when we consider a three-joint manipulator which moves in a two-dimensional plane (see Fig. 1), the direct kinematics are easy to calculate, but for the inverse kinematics there is an infinite number of solutions. Therefore, in the case of a redundant system, additional constraints are necessary to permit a unique solution.

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References

  • Burkamp, Ch. (1996).Untersuchungen zum Lernen in rekurrenten künstlichen Netzen. Diplomarbeit Universität Bielefeld.

    Google Scholar 

  • Cruse, H. (1986). Constraints for joint angle control of the human arm. Biological Cybernetics 54:125–132.

    Article  Google Scholar 

  • Cruse, H. , & U. Steinkühler (1993). Solution of the direct and inverse kinematic problems by a common algorithm based on the mean of multiple computations. Biological Cybernetics 69:345–351.

    Article  Google Scholar 

  • Cruse, H. , & U. Steinkühler (1993). Solution of the direct and inverse kinematic problems by a common algorithm based on the mean of multiple computations. Biological Cybernetics 69:345–351.

    Article  Google Scholar 

  • Cruse, H. , C. Bartling, J. Dean, T. Kindermann, J. Schmitz, M. Schumm, & H. Wagner (1996). Simple solutions to complex problems by exploitation of the physical properties. In P. Maes, M. J. Mataric, J. A. Meyer, J. Pollack, & S. W. Wilson (eds. ),From animals to animats 4. Proceed, of the Fourth Intern. Conf. on Simulation of Adaptive Behavior (pp. 84–93). Cambridge, MA: MIT Press.

    Google Scholar 

  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, USA, 79:2554–2558.

    Article  MathSciNet  Google Scholar 

  • Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, USA, 81:3088–3092.

    Article  Google Scholar 

  • Kawato, M. , & H. Gomi (1992). The cerebellum and VOR/OKR learning models. Trends in Neuroscience 15:445–453.

    Article  Google Scholar 

  • Kawato, M. (1992). Optimization and learning in neural networks for formation and control of coordinated movement. In D. Meyer & S. Kornblum (eds. ),Attention and performance, XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience —A silver jubilee (pp. 821–849). Cambridge, MA: MIT Press.

    Google Scholar 

  • Kawato, M. (1996). Bidirectional theory approach to integration. In T. Inui & J. McClelland (eds. ),Attention and performance XVI (pp. 335–367). Cambridge, MA: MIT Press

    Google Scholar 

  • Morasso, P. , & V. Sanguineti (1994a). Self-organizing topographic maps and motor planning. In D. Cliff, P. Husbands, J. A. Meyer, & S. W. Wilson (eds. ),From animals to animats 3 (pp. 214–220). Cambridge, MA: MIT Press.

    Google Scholar 

  • Morasso, P. , & V. Sanguineti (1994b). Self-organizing body-schema for motor planning. Journal of Motor Behavior 27:52–66.

    Article  Google Scholar 

  • Steinkühler, U. (1994).MMC-Modelle zur Lösung kinematischer Aufgabenstellungen eines redundanten Manipulators. Dissertation, Universität Bielefeld, Germany.

    MATH  Google Scholar 

  • Steinkühler, U. , & H. Cruse (1998). A holistic model for an internal representation to control the movement of a manipulator with redundant degrees of freedom. Biological Cybernetics 79:457–466.

    Article  Google Scholar 

  • Yoshikawa, T. (1985). Manipulatability and redundancy of robotic mechanism. Proceedings of the 1985 IEEE (pp. 1004–1009). Silver Spring, MD: Computer Society Press.

    Google Scholar 

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© 2000 Springer Science+Business Media Dordrecht

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Steinkühler, U., Burkamp, C., Cruse, H. (2000). MMC — A Holistic System for a Nonsymbolic Internal Body Representation. In: Cruse, H., Dean, J., Ritter, H. (eds) Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic, Volume 1, Volume 2 Prerational Intelligence: Interdisciplinary Perspectives on the Behavior of Natural and Artificial Systems, Volume 3. Studies in Cognitive Systems, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0870-9_36

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  • DOI: https://doi.org/10.1007/978-94-010-0870-9_36

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-3792-1

  • Online ISBN: 978-94-010-0870-9

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