Fuzzy Logic Controller-Based Vacuum Cleaner 🌟

This repository contains the project report and MATLAB outputs for a Fuzzy Logic Controller (FLC) designed for a robotic vacuum cleaner. The intelligent system adapts its cleaning strategy based on environmental inputs such as dirtiness level and proximity to obstacles, demonstrating the power of fuzzy logic in automation.

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📝 Problem Statement

Develop a Fuzzy Logic Controller for a robotic vacuum cleaner using MATLAB. The controller:

• Dynamically adjusts cleaning speed and patterns based on real-time inputs.
• Avoids obstacles efficiently.
• Adapts cleaning strategies based on environmental conditions.
• Uses fuzzy rules and membership functions for intelligent decision-making.

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📂 Repository Structure

• Fuzzy_PBL.docx: Detailed project report, including:
  • Problem statement.
  • Theoretical explanation of fuzzy logic.
  • MATLAB code and outputs.
  • Flowcharts, algorithms, and conclusions.
• README.md: Documentation for the project.

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📘 Report Highlights

MATLAB Code and Outputs

The MATLAB implementation includes:

1. Membership Function Editors:

   • Define input and output variables.
   • Design fuzzy sets for dirtiness level, obstacle proximity, and cleaning speed.

2. Rule Viewer:

   • Displays fuzzy if-then rules in action.

3. Surface Viewer:

   • Illustrates the relationship between input variables and output decisions.

4. Fuzzy Logic Designer:

   • Simulates the system and evaluates fuzzy inference rules.

Outputs

• Visualizations from MATLAB's Fuzzy Logic Toolbox, such as rule viewers, membership functions, and surface plots, are documented in the project report.

Python Code

• The project also includes a Python implementation of the fuzzy logic system using the scikit-fuzzy library.

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🌟 Features

• Dynamic Control: Uses fuzzy logic for real-time decision-making.
• Obstacle Avoidance: Intelligent navigation through obstacles.
• Multi-Platform: MATLAB for simulation, Python for real-world implementation.
• User-Friendly Interface: Simulates fuzzy inference and provides interpretable outputs.

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🔧 How to Run

MATLAB Implementation

1. Open MATLAB.
2. Use the code provided in Fuzzy_PBL.docx to set up and run the fuzzy logic system.
3. Visualize the outputs using:
   • Membership Function Editors.
   • Rule Viewers.
   • Surface Viewers.