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FUZZY LOGIC BASED WALL
FOLLOWING ROBOT
Submitted by:
Anshuman Setu
Richa Rashmi
Samrudh K
Setu Ashwin
Under the Guidance of:
RachanaCS
Introduction
 To develop an autonomous robot capable of navigating around a room by
following a wall.
 Inputs are proximity measurements from the wall.
 Outputs are the speeds of the rear motor.
 Using an embedded fuzzy logic controller to determine the response to the
inputs.
 The fuzzy logic controller is programmed to handle real world uncertainties.
The Working.
 The type of control we could use is open loop or a closed loop system.
 Open loop would result in large errors, therefore we opted for a closed
loop system.
 P (proportional) control, PI (proportional integral) control, and PID
(proportional integral derivative) control, fuzzy logic control was
selected as it was easiest to Implement for a highly nonlinear model.
 Fuzzy logic gives the computer power to reason like a human.
 PID control requires a lot of processing power and memory thus making
it more expensive.
 Fuzzy logic achieves better results with less complexity.
Fuzzy Logic a Intro
“We must exploit our tolerance for imprecision”
-Lotfi
Zadeh
 Deals with uncertainties that occur in real life.
 Gives power to a computer to think and reason like a human.
 Useful in non-linear control applications.
 It exploits the tolerance to imprecision.
The working.
Fuzzy Inference System (FIS):
Fuzzifier
Inference
Engine
Knowledge
base
Defuzzifier
Crisp
Fuzzy Fuzzy
Crisp
The working.
Whole System
Sensor
Readings
Calculation of
error
Fuzzy
Logic
Controller
Robot
Components to use
Hardware:
 Servo Motor
 Ultrasonic sensors
 Microprocessor (ATMEGA32)
Software:
 MATLAB
 Simulink
Ultrasonic Sensors
 Devantech SRF04 ultrasonic range finder,
gives an accurate measurement from 3cm to
3m.
 The transmitter works by transmitting a pulse
of sound outside the range of human hearing
at 40 KHz.
 This pulse travel at the speed of sound away from
the ranger in a cone shape and the sound reflects back to the ranger
from any object in the path of sonic wave.The ranger pauses for a
brief interval after the sound is transmitted and then awaits the
reflected sound in the form of an echo.
The Progress: Modeling and Simulation
 Fuzzy logic controller
 FIS System
 Rules
 Fuzzy Output
Fuzzy logic Based Wall(1)
Fuzzy logic Based Wall(1)
Fuzzy logic Based Wall(1)
Fuzzy logic Based Wall(1)
Fuzzy logic Based Wall(1)
Fuzzy logic Based Wall(1)
Scope for improvement
 Instead of wall followers we could make them corridor
followers.
 Use of a faster microcontroller for faster response.
Applications.
 Interoffice mail delivery
 Exploration of buildings in disasters areas
 Autonomous cars
 Army of such robots to map indoor spaces.
THANK YOU

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Fuzzy logic Based Wall(1)

  • 1. FUZZY LOGIC BASED WALL FOLLOWING ROBOT Submitted by: Anshuman Setu Richa Rashmi Samrudh K Setu Ashwin Under the Guidance of: RachanaCS
  • 2. Introduction  To develop an autonomous robot capable of navigating around a room by following a wall.  Inputs are proximity measurements from the wall.  Outputs are the speeds of the rear motor.  Using an embedded fuzzy logic controller to determine the response to the inputs.  The fuzzy logic controller is programmed to handle real world uncertainties.
  • 3. The Working.  The type of control we could use is open loop or a closed loop system.  Open loop would result in large errors, therefore we opted for a closed loop system.  P (proportional) control, PI (proportional integral) control, and PID (proportional integral derivative) control, fuzzy logic control was selected as it was easiest to Implement for a highly nonlinear model.  Fuzzy logic gives the computer power to reason like a human.  PID control requires a lot of processing power and memory thus making it more expensive.  Fuzzy logic achieves better results with less complexity.
  • 4. Fuzzy Logic a Intro “We must exploit our tolerance for imprecision” -Lotfi Zadeh  Deals with uncertainties that occur in real life.  Gives power to a computer to think and reason like a human.  Useful in non-linear control applications.  It exploits the tolerance to imprecision.
  • 5. The working. Fuzzy Inference System (FIS): Fuzzifier Inference Engine Knowledge base Defuzzifier Crisp Fuzzy Fuzzy Crisp
  • 6. The working. Whole System Sensor Readings Calculation of error Fuzzy Logic Controller Robot
  • 7. Components to use Hardware:  Servo Motor  Ultrasonic sensors  Microprocessor (ATMEGA32) Software:  MATLAB  Simulink
  • 8. Ultrasonic Sensors  Devantech SRF04 ultrasonic range finder, gives an accurate measurement from 3cm to 3m.  The transmitter works by transmitting a pulse of sound outside the range of human hearing at 40 KHz.  This pulse travel at the speed of sound away from the ranger in a cone shape and the sound reflects back to the ranger from any object in the path of sonic wave.The ranger pauses for a brief interval after the sound is transmitted and then awaits the reflected sound in the form of an echo.
  • 9. The Progress: Modeling and Simulation  Fuzzy logic controller  FIS System  Rules  Fuzzy Output
  • 16. Scope for improvement  Instead of wall followers we could make them corridor followers.  Use of a faster microcontroller for faster response.
  • 17. Applications.  Interoffice mail delivery  Exploration of buildings in disasters areas  Autonomous cars  Army of such robots to map indoor spaces.