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Intelligent Autonomous Robotics - CMP 494-10

Project-3.mp4
HW-3.mp4

Note: for more demos, please refer to the assets folders. Click on Raw to download the video.

Table of Contents

Project Description

This repository houses our practical assignments and projects, all of which were developed using the Webots Simulator. The content is organized as follows:

  • HW 3 - Implementation of a Finite State Automaton in a Reactive Wheeled Robot for Simple Can Detection
  • Project 1 - Implementation of Artificial Potential Field
  • Project 2 - Implementation of A* Path Planning and Waypoint Navigation in a Static Environment
  • Project 3 - Autonomous Can Detection and Mapping with RRT* and YOLO

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

Technologies Used

The assignments and projects are implemented using Python and the following libraries:

  • 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.

Installation & Usage

  1. Clone this repository:

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

  2. Install the required packages:

    pip install -r requirements.txt

  3. Launch the desired world in Webots

  4. Run the simulation

Note: If YOLO is not already installed, it will be downloaded and installed when running Project 3's simulation. This may cause the simulation to take a while to start.

Credits

The assignments and projects were developed for the Intelligent Autonomous Robotics course at American University of Sharjah by Ahmad Alsaleh, Ahmed Alabd Aljabar, Omar Ibrahim, and Yousef Irshaid.

Immense gratitude is extended to Dr. Michel Pasquier for his invaluable mentorship during the duration of the course.

License

This repository is under the MIT License.