A beginner's guide to robot programming with Python

Let's face it, robots are cool. They're also going to run the world some day, and hopefully, at that time they will take pity on their poor soft fleshy creators (a.k.a. I'm joking of course, but only sort of. In my ambition to have some small influence over the matter, I took a course in autonomous robot control theory last year, which culminated in my building a Python-based robotic simulator that allowed me to practice control theory on a simple, mobile, programmable robot. In this article, I'm going to show how to use a Python robot framework to develop control software, describe the control scheme I developed for my simulated robot, illustrate how it interacts with its environment and achieves its goals, and discuss some of the fundamental challenges of robotics programming that I encountered along the way. The snippets of code shown here are just a part of the entire simulator, which relies on classes and interfaces, so in order to read the code directly, you may need some experience in Python and object oriented programming. Finally, optional topics that will help you to better follow this tutorial are knowing what a state machine is and how range sensors and encoders work. The fundamental challenge of all robotics is this: It is impossible to ever know the true state of the environment. Robot control software can only guess the state of the real world based on measurements returned by its sensors. It can only attempt to change the state of the real world through the generation of control signals.