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Volume 22 Issue 3 - Publication Date: 1 March 2003
 
Neuroethological Concepts and Their Transfer to Walking Machines
 
Volker Dürr, André Krause, Josef Schmitz and Holk Cruse Abteilung Biologische Kybernetik und Theoretische Biologie, Fakultät für Biologie, Universität Bielefeld, Postfach 10 01 31, D-33501 Bielefeld, Germany
 
A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered.
ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences of the various output parameters are comprised by the weight matrix of the net. Based upon behavioral observations of insect walking, we further demonstrate how a decentralized network of separate modules, each one described by an ANN, can account for adaptive behavior. Complex coordination patterns of several manipulators are controlled by imposing simple interaction rules between limbs, and by exploiting the interaction of the body with its physical environment. Finally, we discuss the technical use of leg-like active tactile sensors for obstacle detection, and we show how specific design of such active sensors may increase efficiency of walking on rough terrain. Applied to active sensors, an example of parallel, self-organizing forward models on the basis of extended Kohonen maps is presented to emphasize the potential of adaptive forward models in motor control.
 
Multimedia Key
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Example One: Behavioral experiment with Crausius morosus. (5.3 MB)
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Example Two: Kinematic stick insect model walking across obstacles. (6.4 MB)
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Example Three: Performance of the WalkNet controller in response to sudden amputation of the right middle leg (a bird severs the tibia). (3.8 MB)
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Example Four: Efficiency evaluation of an active tactile sensor that is modelled to match a stick insect antenna. (5.2 MB)
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Example Five: Tracking performance of an active tactile sensor driven by a set of parallel, self-organizing extended Kohonen maps. (4.1 MB)
 
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