Neural Control of a Mobile Robot
by Jonathan Connell
The human brain is very complex. There are billions of neurons and trillions of connections between them. Even though many areas have a uniform structure, there are still several hundred architecturally distinct regions. This makes simulating a human being very difficult. By contrast, there are a number of insects and marine animals that have far fewer neurons. Many of these have been studied in detail and scientists have a good idea of how the various components of their brains are connected together. There is also an extensive body of experimental data detailing what sort of behaviors are present in each animal and how various groups of neurons interact to perform the necessary computations. Thus, at this time there is a better basis for building robotic models of simple creatures than humans. Yet, since man evolved from these simple creatures, the knowledge gained from such an endeavor should ultimately lead us to a better understanding of the human mind itself.
Here, we investigate how animal-like control systems might plausibly be implemented using neuron-like elements. The first half of this article describes the nature and capabilities of elementary reflexive systems. It also explains how a collection of simple threshold units can perform the necessary processing. The second half of the article shows how to construct an actual robot, "Muramator", using electronics to model the relevant biology. The robot's primary goal is to follow around the edges of its world. However, by modifying some of the design parameters, this creature's overall behavior can be altered in interesting ways.
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