| Robotic
control Using Fuzzy Logic |
INTROUCTION
Automatic guided vehicle or
mobile robots is an intelligent machine that has intelligence to determine its
motion starts according to the environment conditions. For an AGV to operate it
must sense its environment be able to plan its operations and then act based on
this plan.
The running environment could be varied such as the path orientation,
road flatness, obstacle position, road surface friction etc. There are great many
uncertainties of what condition will emerge during its operation. Thus a new control
method other than the conventional control method is demanded to manage the response
of the whole system.
In the last years, fuzzy logic has been applied
mobile robot and autonomous vehicle control significantly. The best arguments
supporting fuzzy control are the ability to cope with imprecise information in
heuristic rule based knowledge and sensor measurements.
Fuzzy logic can help design robust individual behaviors units. Fuzzy logic controllers
incorporate heuristic control knowledge. It is convenient choice when a precise
linear model of the system to be controlled cannot be easily found. Another advantage
of fuzzy logic control is to use fuzzy logic for representing uncertainties, such
as vagueness or imprecision which cannot be solved by probability theory. Also
fuzzy logic offers greater flexibility to user, among which we can choose the
one that best, fits the type of combination to be performed.
WHAT
IS FUZZY LOGIC?
Fuzzy
logic is another class of AI, but its history and applications are more recent
than those of the expert systems (ES). According to George Boole, human thinking
and decisions are based on "Yes / No" reasoning or "1 / 0 "
logic. According to Boolean logic developed and expert system principles were
formatted based on Boolean logic. It has been argued that human thinking does
not always follow crisp "Yes / No " logic, but is often vague, quantitative,
uncertain, imprecise or fuzzy in nature.
For example in terms of " Yes / No " logic, a thinking rule may be
"
If it is not raining AND outside temperature is less than 80 F ,THEN take a sight
seeing trip for more than 100 miles"
In
actual thinking it might be
"IF weather is good AND outside temperature
is mild THEN take a long sight seeing trip".
Based on the nature
of human thinking, Lotfi Zadeh, a computer scientist at the university of California,
Berkeley, originated "The Fuzzy Logic" or "Fuzzy Set Theory"
in 1965. In the beginning, he was highly criticized by professional community,
but gradually this emerged as an entirely new discipline of AI. The general methodology
of reasoning in fuzzy
logic and expert system by "IF.. THEN…" statements or rules are
the same, therefore it is often called "Fuzzy Expert System "
A fuzzy logic can help to supplement an ES and it is sometime hybrid with the
latter to solve complex problems. Fuzzy logic has been successfully applied in
process control, modeling, estimation, identification diagnostics, military science,
stock market prediction etc.
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