FUZZY LOGIC CONTROLLER

FEBRUARY-APRIL 2018

LINK TO PROJECT REPORT

OBJECTIVE

Design of a Fuzzy Logic Controller (FLC) for mobile robot navigation in an unknown, static or dynamic envinronments using the Tracking FLC and Obstacle Avoidance FLC and implementation of the same on TurtleBot2 robot.

RESEARCH ASPECTS

  Accessing stereovision point-cloud data and laserscan data
  Defining membership functions for Obstacle Avoidance FLC and Tracking FLC inputs - distance between the robot and the target, distance between the robot and obstacle and the angular presence of the same
   Fuzzy Inference System design and defuzzification techniques
  Design of If-Else fuzzy rules for OAFLC and TFLC

Fuzzy Inference System - Controller Design

Membership Function for TFLC - Distance between robot and target

METHODOLOGY

The Takagi-Sugeno-Kang fuzzy inference technique and the Centroid defuzzification methods are used to implement our proposed controller

The TSK approach computes the output of the If-Else rules as a linear expression made up of weighted conditional components. Elaborately, the FIS setup processes all If-Else conditional statements with the weights generated on the basis of the membership functions and computes a new weight for execution of the condition

The Centroid defuzzification process computes a normalized weight distribution for conditions and thereafter their weighted sum to generate final numerical output values

The Gazebo simulation environment was used with a customized design of the world cluttered with obstacles

Weighted behavior fusion for both FLCs to obtain final robot commands.


Behavior Fusion

RESULTS

   The robot was successfully able to navigate through the environment and avoid obstacles enroute reaching the target.
  The controller was tested on several terminal states as well as environments


Video Demonstrations of implemented FLC

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Copyright @Akshay Kumar | Last Updated on 12/30/2017

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