Page 62 - Designing Autonomous Mobile Robots : Inside the Mindo f an Intellegent Machine

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CHAPTER

Thinking More Clearly

Through Fuzzy Logic

Of all the software concepts touched upon in this book, fuzzy logic is one of the most
valuable and most widely misunderstood. These misunderstandings are at least in
part a consequence of the fact that the term fuzzy is not transparently descriptive. The
basic concept of fuzzy logic is so simple that it amounts to little more than common
sense. If you understand the mathematics (geometry) behind a straight line, you can
master fuzzy logic.

The purpose of fuzzy logic is to provide the best possible “guess” at the value of something
that cannot be measured directly, but which can be inferred from a combination of inputs.
For this reason, a system that accomplishes this is sometimes referred to as an “inference
engine.”

To understand the place of fuzzy logic, we will first consider Boolean logic as it has
been used in sensor based systems. In Boolean logic, all inputs, outputs, and calcula-
tions deal with two states; ‘true’ and ‘false’. These states may be expressed as ‘1’ and
‘0’, or –1 (FFFFh) and ‘0’, but they always represent the two Boolean states. If inputs
are not Boolean to begin with, then they are compared to a threshold to convert
them to Boolean states before being manipulated by the Boolean logic.
A simple example of this process can be seen in a “dual technology” motion detector
used in the security industry. In the 1970’s and early 1980’s, several motion detector
technologies vied for market supremacy. These included light-beam, ultrasonic, pas-
sive infrared (PIR), and microwave based systems. The benchmark for effectiveness
was measured by how sensitive the system could be set without experiencing an
unacceptable rate of false alarms. The PIR and microwave technologies eventually
emerged as dominant, but both could be false alarmed under some circumstances.

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