🤖 Intelligent Mobile Robotics

A MATLAB-based collection of mobile robotics algorithms and simulations, including Bug Algorithms, Potential Field Methods, and Wheeled Mobile Robot Kinematics with RRT & Kalman Filtering.

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📂 Repository Structure

Intelligent-Mobile-Robotics/
│
├── BugAlgorithms/
│   ├── Bug1.m
│   ├── Bug2.m
│   ├── TangentBug.m
│   ├── MyBug.m
│   └── Documentation.pdf
│
├── PotentialFieldMethods/
│   ├── StandardPotentialField.m
│   ├── MyPotentialField.m
│   ├── NavigationFunction.m
│   └── Documentation.docx
│
├── Kinematics_RRT_Kalman/
│   ├── TricycleTrailer_Kinematics.m
│   ├── RRT_PathPlanning.m
│   ├── KalmanFilter_Implementation.m
│   └── Documentation.pdf
│
└── README.md

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🧭 Module Overview

🚦 Module 1 — Bug Algorithms

Includes:

• Bug 1
• Bug 2
• Tangent Bug
• MyBug (Custom hybrid algorithm)

Highlights of MyBug:

• 📌 Segment-based obstacle boundary division
• 🔄 Dynamic segment switching & skipping
• 🗂 Memory-based obstacle geometry storage
• 🏎 More efficient paths than standard Bug methods

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🛰 Module 2 — Potential Field Methods

Implements:

• Standard Potential Field
• MyPotential Algorithm
• Navigation Function Approach

MyPotential Advantages:

• Hybrid attractive potential (quadratic near goal, exponential far away)
• Gaussian repulsive potential for smooth avoidance
• Reduced local minima issues
• Smoother & more direct paths

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🚗 Module 3 — Kinematics, RRT, and Kalman Filter

Covers:

• Kinematic modeling of a tricycle robot with trailer
• RRT for path planning
• Kalman Filter for noise correction

Key Features:

• Models non-holonomic wheeled robots
• Generates collision-free paths in complex environments
• Corrects noisy trajectories with Kalman filtering

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🛠️ Technologies

• MATLAB — Simulation and implementation
• Path Planning — Bug, Potential Field, RRT
• State Estimation — Kalman Filter
• Robotics Models — Tricycle with trailer

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🚀 How to Run

git clone https://github.com/rabbia67/Intelligent-Mobile-Robotics.git

1. Open MATLAB.
2. Navigate to the relevant module folder.
3. Run the main .m script.
4. Adjust parameters inside the code to explore different scenarios.

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📊 Results Summary

Module	Main Improvement
Bug Algorithms	Shorter paths via dynamic segment switching
Potential Fields	Reduced local minima, smoother navigation
Kinematics & Planning	Kalman filter improves accuracy

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📄 License

For educational & research purposes. Please cite if using in projects or publications.