C++ Neural Networks and Fuzzy Logic: Preface

Mobile Robot Navigation

C. Lin and C. Lee apply a multivalued Boltzmann machine, modeled by them, using an artificial magnetic

field approach. They define attractive and repulsive magnetic fields, corresponding to goal position and

obstacle, respectively. The weights on the connections in the Boltzmann machine are none other than the

magnetic fields.

C++ Neural Networks and Fuzzy Logic:Preface

Chapter 17 Further Applications

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They divide a two−dimensional traverse map into small grid cells. Given the goal cell and obstacle cells, the

problem is to navigate the two−dimensional mobile robot from an unobstructed cell to the goal quickly,

without colliding with any obstacle. An attracting artificial magnetic field is built for the goal location. They

also build a repulsive artificial magnetic field around the boundary of each obstacle. Each neuron, a grid cell,

will point to one of its eight neighbors, showing the direction for the movement of the robot. In other words,

the Boltzmann machine is adapted to become a compass for the mobile robot.

A Classifier

James Ressler and Marijke Augusteijn study the use of neural networks to the problem of weapon to target

assignment. The neural network is used as a filter to remove unfeasible assignments, where feasibility is

determined in terms of the weapon’s ability to hit a given target if fired at a specific instant. The large number

of weapons and threats along with the limitation on the amount of time lend significance to the need for

reducing the number of assignments to consider.

The network’s role here is classifier, as it needs to separate the infeasible assignments from the feasible ones.

Learning has to be quick, and so Ressler and Augusteijn prefer to use an architecture called the

cascade−correlation learning architecture, over backpropagation learning. Their network is dynamic in that

the number of hidden layer neurons is determined during the training phase. This is part of a class of

algorithms that change the architecture of the network during training.