Autonomous Robotics

Abstract

1. Introduction

More details could be found in the requirements and answers reports found in the folder of each assignment.

2. Objective

A fully autonomous robot with obstacle avoidance (APF), path planning (A* & RRT*), and object detection (YOLO)

This project demonstrates how Python, YOLO can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• OpenCV: Used for real-time computer vision to read and manipulate images and videos.
• NumPy: Used for numerical computations in Python.
• PyTorch: An open-source machine learning library used to create and train the neural network.
• Matplotlib: Used for creating static, animated, and interactive visualizations in Python.
• Scikit-learn: A machine learning library in Python. It features various classification, regression and clustering algorithms.
• YOLOv5: State-of-the-art open-source object detection model from Ultralytics.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Ahmad-Alsaleh/Autonomous-Robotics.git
cd Autonomous-Robotics

1. Clone this repository:
2. Install the required packages:
3. Launch the desired world in Webots
4. Run the simulation

git clone https://github.com/Ahmad-Alsaleh/Autonomous-Robotics

pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Ahmad-Alsaleh and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.