The document presents a presentation on robotics control using fuzzy logic. It introduces fuzzy logic and how it handles partial truths between completely true and completely false. It also discusses fuzzy sets and membership functions. The presentation then covers fuzzy logic control and its applications to robotics, including defining robots and discussing different types. It concludes with a summary of how the project uses fuzzy logic to realize reactive robot navigation behaviors.
2. Fuzzy Logics
Lotfi A. Zadeh, a professor of UC Berkeley in California, soon
to be known as the founder of fuzzy logic.
Fuzzy logic has rapidly become one of the most successful of
today's technologies for developing sophisticated control
systems.systems.
Fuzzy logic is a superset of Boolean logic that has been
extended to handle the concept of partial truth- truth values
between "completely true" and "completely false".
Fuzzy logic can be implemented in hardware, software or a
combination of both.
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3. Fuzzy Set and Membership
Functions
Crisp Membership Function Fuzzy Membership Function
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Crisp Membership Function
Trapezoidal Membership Function
ฯ- shaped membership functionsGaussian Membership Function
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5. Robotics
A robot is a machine that looks and acts like a human being.
Robots are defined as man-made mechanical devices that can
move by themselves, whose motion must be modeled, planned,
sensed, actuated and controlled, and whose motion behaviorsensed, actuated and controlled, and whose motion behavior
can be influenced by โprogrammingโ.
Joseph F. Engelberger is known as the โFather of Roboticsโ
After the technology explosion during World War II, in 1956,
George C. Devol, Norman Schafler and Joseph F. Engelberger
made a serious and commercial effort to produce a robot and
the name of the first robot is โUnimateโ.
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6. Types of Robots
Mobile Robots Industrial Robots
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Mobile Robots Industrial Robots
Autonomous Robots
Remote Control Robots
Virtual Robots
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13. Summery
In this project, we use fuzzy logic to realize the reactive
behaviors for robot navigation. The method can
effectively coordinate conflicts and competitions
among multiple reactive behaviors by weighting them
and this coordination ability is nearly independent of aand this coordination ability is nearly independent of a
dynamic environment due to it robustness.
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