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|Title: ||Using evolutionary artificial neural networks to design hierarchical animat nervous systems.|
|Authors: ||McMinn, David|
|Supervisors: ||Maxwell, Grant M.|
|Keywords: ||Artificial neural networks|
Artificial nervous system
|Issue Date: ||Dec-2001|
|Publisher: ||The Robert Gordon University|
|Abstract: ||The research presented in this thesis examines the area of control systems for
robots or animats (animal-like robots). Existing systems have problems in that they
require a great deal of manual design or are limited to performing jobs of a single
type. For these reasons, a better solution is desired.
The system studied here is an Artificial Nervous System (ANS) which is
biologically inspired; it is arranged as a hierarchy of layers containing modules
operating in parallel. The ANS model has been developed to be flexible, scalable,
extensible and modular. The ANS can be implemented using any suitable
technology, for many different environments.
The implementation focused on the two lowest layers (the reflex and action
layers) of the ANS, which are concerned with control and rhythmic movement.
Both layers were realised as Artificial Neural Networks (ANN) which were created
using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the
position of an actuator (such as linear actuators or D.C. motors). The action layer
performed the task of Central Pattern Generators (CPG), which produce rhythmic
patterns of activity. In particular, different biped and quadruped gait patterns were
created. An original neural model was specifically developed for assisting in the
creation of these time-based patterns.
It is shown in the thesis that Artificial Reflexes and CPGs can be configured
successfully using this technique. The Artificial Reflexes were better at
generalising across different actuators, without changes, than traditional
controllers. Gaits such as pace, trot, gallop and pronk were successfully created
using the CPGs. Experiments were conducted to determine whether modularity in
the networks had an impact. It has been demonstrated that the degree of
modularization in the network influences its evolvability, with more modular
networks evolving more efficiently.|
|Appears in Collections:||Theses (Engineering)|
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