Bio-inspired Emergent Control Of Locomotion Systems (World by Mattia Frasca

By Mattia Frasca

This publication offers with locomotion regulate of biologically encouraged robots learned via an analog circuital paradigm as mobile nonlinear networks. It provides a normal method for the keep an eye on of bio-inspired robots and several other case stories, in addition to describes a brand new method of movement keep watch over and the comparable circuit architecture.Bio-inspired Emergent keep watch over of Locomotion structures presents researchers with a advisor to the basics of the subjects. additionally, neuro-biologists and physiologists can use the e-book as a place to begin to layout man made buildings for checking out their organic hypotheses at the animal version.

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Extra info for Bio-inspired Emergent Control Of Locomotion Systems (World Scientific Series on Nonlinear Science, Series A, Vol. 48)

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If simplified chemical synapses are used instead of diffusive synapses and so if the approach depicted in Sec. 2 is adopted for the CPG realization] three different template sets will be obtained. Indeed, the templates depend on the cell (they are space-variant) and on the pattern. On the other hand, the CNN able to generate and control the whole locomotion dynamics is often made up of a low number of neurons. Therefore the space-variant template approach does not constitute a drawback. Moreover, the advantage of this approach is that the network structure] as well as all the cell-leg connections are fixed.

1 Behavior of the CNN neuron driven b y a periodic forcing signal The concept of synchronization is an important topic for the study of nonlinear systems. Self-sustained oscillators can be entrained by a weak external periodic forcing signal, provided that the forcing frequency is close to that of self-sustained oscillations. For a suitable range of the parameters of the forcing signal (frequency and amplitude), called the synchronization region (or Arnold tongue), the oscillations of the entrained nonlinear system have the same frequency as the forcing signal.

135 .... ~ ...... ......... .... .... 14 145 15 Time (samples) Fig. 13 Waveforms of variable the half-bound of the hare. ~ 1 I I 155 ... I ... .. 16 165 x 10' for, the ~ 4 motor-neurons of the CPG generating Finally, it is worth remarking that the MTA-CNN for real-time locomotion control in bio-inspired robots allows us to overcome a limit of the RD-CNN implementation for CPGs: the absence of chemical synaptic input which prevented us from obtaining different locomotion patterns without varying the CNN structure.

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