Jupyter Notebooks for robotics courses
Introductory resource for undergraduate robotics courses, including a collection of Jupyter notebooks covering many fundamental topics related to mobile robots. Each notebook includes some theoretical background on the topic at hand, and coding tasks and demos written in Python. This resource is an Student version, therefore the coding exercises do not include solutions. If you are a Lecturer interested in the complete version of the notebooks, you can ask for them individually.
Developed by Samuel Felipe Baltanas, Jose Raul Ruiz, and Javier Gonzalez, Machine Perception and Intelligent Robotics group (MAPIR), University of Malaga (Spain).
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Topics covered
The notebooks are classified by the topic each covers. There are an introduction on the notebooks plus 7 different topics:
- Introduction
- Fundamentals
- Motion
- Sensing
- Localization
- Mapping
- SLAM
- Motion planning
Dependencies
The notebooks have been tested using Python 3.6.7, Jupyter Notebooks and the exact library setup described in requirements.txt.
Nevertheless, it depends in the following set of libraries:
- Numpy
- Scipy
- Matplotib
- ipywidgets (additional installation steps may be required: link)
- Pandas
The use of JupyterLab at this moment is discouraged, as some visualizations do not work within it.