Martins6 / mobile_robotics_project_1

Assignments from UFPR's course "Introduction to Mobile Robotics".

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Mobile Robotics course assignments

The course was held at the Universidade Federal do Paraná (UFPR) in 2023/2. It was taught by Professor Dr. Eduardo Todt.

Assignments

Assignment 1: Differential Drive

All the actual files for this project is found on the differential_drive_robot folder. The project consists of a differential drive robot that moves in a 2D environment. It was implemented using numpy and matplotlib.

Differential drive robots are important for mobile robotics because they are simple and easy to implement. They are also very common in the industry, being used in robots such as the Turtlebot.

Assignment 2: Image Classification

All the actual files for this project is found on the image_classification folder. The project consists of a convolutional neural network that classifies images from the CIFAR-10 dataset. It was implemented using the PyTorch framework, also using Pytorch Lightning to make the code more readable and easier to debug.

To able to classify image is important for mobile robotics for the reason of being able to identify objects in the environment. This is important for tasks such as object detection and localization, which are important for autonomous navigation.

Assignment 3: Mapping

All the actual files for this project is found on the mapping folder. The project consists of a mapping algorithm that uses LiDAR. Quite simple but good to get the feeling of how mapping works.

Mapping is important for mobile robotics because it allows the robot to know where it is in the environment. This is important for tasks such as autonomous navigation.

Assignment 4: Autonomous Navigation + Depth Estimation

All the actual files for this project is found on the autonomous_navigation folder. The project consists of a robot that navigates autonomously in a 3D environment using Turtlebot3. Also, I've accessed the images from the camera and used them to estimate the depth of the environment via a deep learning model.

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Assignments from UFPR's course "Introduction to Mobile Robotics".

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