A SUBSYSTEM APPROACH TO DEVELOPING A BEHAVIORAL BASED HYBRID NAVIGATION SYSTEM FOR AUTONOMOUS VEHICLES
There does not exist one paradigm in machine intelligence that can
function as a Black-box for complex tasks.
This is especially true for behavior based controllers.
By implementing a subsystem based organization, each element of the
behavior controller may be constructed using the machine intelligence
paradigm best suited for that task.
In this thesis a behavior based intelligent navigations system will be
developed for use in mobile robotics.
This thesis will also contain a review, history, and enhancement of each
of the basic paradigms and training methods.
Topics such as back-propagation neural networks, fuzzy associative maps,
and genetic algorithms will be presented.
Enhancements to the training speed of back-propagation, and a new method
for fuzzy clustering will be discussed.
Also, presented in this work will be methods for constructing
computational models from physical ones.
All of the programming code in this work is based on a C++ Class Library
that was developed by the author as an independent project.
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