Useful Code for the several Robotics fields. It is divided into the following major branches:
- Engineering.
- Intelligence.
- Tracking and Sensing.
Coding and Compiling Standards used in this project.
- MATLAB: System Modeling.
- MATLAB: Interactive Control Systems Tutorial. Learn essential skills for modeling, analyzing and designing control systems in MATLAB and Simulink.
- Robotics Toolbox for MATLAB by Peter Corke. Functions that are useful for the study and simulation of classical arm-type robotics. It also has a GitHub repository.
- Tutorial on Control Theory. Complete tutorial by Stefan Simrock.
- Stanford: EE392m: Control Engineering in Industry Material for the course about Control Engineering in Industry from the Spring Quarter 2004-2005.
- Youtube: Brian Douglas' Channel on Control Theory. Excellent videos about Control Theory from basics to advanced topics.
- Control Theory Workshop. A students manual from Texas Instruments summarizing the most important concepts of Control Theory.
- Circuit Theory. Wikibook with the basics of Circuit Theory.
- Control Systems. Wikibook of automatic Controly Systems and Control Systems Engineering with Classical and Modern Techniques, and Advanced Concepts.
- Embedded Systems. Wikibook about microcontrollers, real-time operating systems and their programming.
- Electronics. Wikibook about the basics of Electronics.
Books
- Robot Manipulator Control. Theory and Practice; Frank L. Lewis, Darren M. Dawson and Chaouki T. Abdallah; Marcel Dekker Inc. 2004.
- Control Engineering Problem with Solutions; Derek P. Atherton; bookboon.com. 2013.
- Electrical and Electronic Engineering Books. A list with relevant bibliography about Electricity and Electronics.
- Automation and Robotics; Juan Manuel Ramos Arreguín; I-Tech. 2008.
- Springer Handbook of Robotics edited by Bruno Siciliano and Oussama Khatib is the best compendium of Robotics out there.
- Youtube: How Bayes Theorem works (25:09) by Brandon Rohrer.
- Gaussian Processes. Overview of resources concerned with probabilistic modeling, inference and learning based on Gaussian Processes.
- Natural Language Processing with Python. Online version of the NLTK book updated for Python 3 and NLTK3.
- Youtube: Machine Learning Part 1 | SciPy 2016 Tutorial (3:03:54) by Andreas Mueller & Sebastian Raschka.
- Youtube: Machine Learning Part 2 | SciPy 2016 Tutorial (3:08:05) by Andreas Mueller & Sebastian Raschka.
- Youtube Playlist: Machine Learning course 2013 (21 videos) by Nando de Freitas.
- Youtube Playlist: Machine Learning (107 videos) of Coursera by Andrew Ng.
- Youtube Playlist: Machine Learning (160 videos) by mathematicalmonk.
- Youtube Playlist: Neural Networks (4 videos) by Grant Sanderson (a.k.a. 3blue1brown) giving a very clear and intuitive explanation of how NNs work. His channel is worth visiting and subscribing.
- Youtube Playlist: Neural Networks for Machine Learning (78 videos) of Coursera from Geoffrey Hinton.
- Youtube Playlist: Deep Learning course 2015 (16 videos) by Nando de Freitas.
- Deep Learning Tutorials. List of Deep Learning materials.
- Stanford: UFLDL Tutorial. Deep Learning Tutorial by Stanford including programming exercises using MATLAB.
- Youtube: How Convolutional Neural Networks work (26:14) by Brandon Rohrer.
- Youtube: CNNs. Neural Network that Changes Everything (14:16) by Dr. Mike Pound in Computerphile.
- Youtube: Inside a Convolutional Neural Network (15:41) by Dr. Mike Pound in Computerphile.
- Stanford: CNNs for Visual Recognition. Deep dive into details of the Deep Learning architectures with a focus on image classification.
- TUM: Computational Intelligence. CI Class from the TU München, lectured by Prof. Dr. Jörg Conradt.
- How to build a Neural Network Part 1 and Part 2 by Steven Miller.
- Youtube: How Deep Neural Networks Work (24:37) by Brandon Rohrer.
- Neural Networks and Deep Learning. Free online book about NNs and Deep Learning by Michael Nielsen.
- fast.ai is a portal with an incredibly useful tutorial and material for cutting-edge Deep Learning by Jeremy Howard.
Books
- Bayesian Resoning and Machine Learning; David Barber; Cambridge University Press; 2012.
- Gaussian Processes for Machine Learning; Carl Edward Rasmussen and Christopher K. I. Williams; MIT Press; 2006.
- Artificial Intelligence Books. A list with relevant bibliography about Artificial Intelligence.
- The Nature of Code; Daniel Shiffman.
- NLTK book; Steven Bird, Ewan Klein, and Edward Loper. Online.
- The Mathematics of the 3D Rotation Matrix. A practical explanation on the Maths of 3D geometric systems.
- Linköpings Universitet: Sensor Fusion. The most important methods and algorithms of sensor fusion for networking, navigation and tracking applications.
- Sensor Fusion using the Kalman Filter. A simple and concise presentation of the Kalman Filter.
- IMU Data Fusing: Complementary, Kalman, and Mahony Filter. Explanation and basics of IMU data-fusing with the use and comparison of several algorithms (KF, Complementary, Mahony and Madgwick.)
- Youtube Playlist: SLAM Course - WS13/14 (22 videos) by Cyrill Stachniss with available lecture material.
- Programming for robotics: Introduction to ROS. 4 Video-Tutorials prepared by Péter Fankhauser introducing the Robot Operating System.
- Fritzing. Open-source hardware designer.
Books
- Recent developments of Kalman Filter; Vedran Kordic; InTech. 2010.
- Computer Vision: Algorithms and Applications; Richard Szeliski; Springer. 2010.
There is an extra folder Data to store the files used for examples and tests.