This is the fork of the original project PySimIAm from sourceforge.net made by Tim Fuchs (A python port of Jean-Pierre's Matlab program Sim.I.Am from GA Tech.)

• Original URI: http://pysimiam.sourceforge.net/coursera.html
• Local docs: see "doc/coursera.html" in this repository

• PySimIAm robot simulator by Tim Fuchs for Coursera ‘Control of mobile robots’ course:
  A python port of Jean-Pierre's Matlab program Sim.I.Am from GA Tech.
  http://pysimiam.sourceforge.net/coursera.html
• Anaconda (Python + PyQT4 + Numpy, Python all-in-one installer):
  http://continuum.io/downloads
• O'Botics - Updates on robot hardware
  A place where roboticists can collaborate on robot designs,
  code, electronics, and hardware.
  http://o-botics.org/
  https://github.com/o-botics
• Author's GitHub: https://github.com/typograph
• Original PySimIAm project on GitHub
  • Week1: https://github.com/typograph/pysimiam/tree/coursera-week1-2014
  • Week2: https://github.com/typograph/pysimiam/tree/coursera-week2-2014
  • Week3: https://github.com/typograph/pysimiam/tree/coursera-week3-2014
  • Week4: https://github.com/typograph/pysimiam/tree/coursera-week4-2014
  • Week5: https://github.com/typograph/pysimiam/tree/coursera-week5-2014
  • Week6: https://github.com/typograph/pysimiam/tree/coursera-week6-2014
  • Week7: https://github.com/typograph/pysimiam/tree/coursera-week7-2014

• Control of Mobile Robots by Dr. Magnus Egerstedt
  Learn about how to make mobile robots move in effective, safe, predictable,
  and collaborative ways using modern control theory.
  https://www.coursera.org/course/conrob

• Autonomous Navigation for Flying Robots
  In this course, we will introduce the basic concepts for autonomous navigation
  with quadrotors, including topics such as probabilistic state estimation,
  linear control, and path planning.
  https://www.edx.org/course/autonomous-navigation-flying-robots-tumx-autonavx

• Visual Navigation for Flying Robots
  In recent years, flying robots such as autonomous quadrocopters have gained
  increased interest in robotics and computer vision research. For navigating
  safely, these robots need the ability to localize themselves autonomously
  using their onboard sensors. Potential applications of such systems
  include the automatic 3D reconstruction of buildings, inspection and
  simple maintenance tasks, surveillance of public places as well as
  in search and rescue systems.
  http://vision.in.tum.de/teaching/ss2013/visnav2013

• Artificial Intelligence for Robotics. Programming a Robotic Car.
  Learn how to program all the major systems of a robotic car from the leader
  of Google and Stanford's autonomous driving teams. This class will teach you
  basic methods in Artificial Intelligence, including: probabilistic inference,
  planning and search, localization, tracking and control, all with a focus
  on robotics. Extensive programming examples and assignments will apply
  these methods in the context of building self-driving cars.
  https://www.udacity.com/course/cs373