Book Image

Learn Robotics Programming - Second Edition

By : Danny Staple
Book Image

Learn Robotics Programming - Second Edition

By: Danny Staple

Overview of this book

We live in an age where the most complex or repetitive tasks are automated. Smart robots have the potential to revolutionize how we perform all kinds of tasks with high accuracy and efficiency. With this second edition of Learn Robotics Programming, you'll see how a combination of the Raspberry Pi and Python can be a great starting point for robot programming. The book starts by introducing you to the basic structure of a robot and shows you how to design, build, and program it. As you make your way through the book, you'll add different outputs and sensors, learn robot building skills, and write code to add autonomous behavior using sensors and a camera. You'll also be able to upgrade your robot with Wi-Fi connectivity to control it using a smartphone. Finally, you'll understand how you can apply the skills that you've learned to visualize, lay out, build, and code your future robot building projects. By the end of this book, you'll have built an interesting robot that can perform basic artificial intelligence operations and be well versed in programming robots and creating complex robotics projects using what you've learned.
Table of Contents (25 chapters)
1
Section 1: The Basics – Preparing for Robotics
7
Section 2: Building an Autonomous Robot – Connecting Sensors and Motors to a Raspberry Pi
15
Section 3: Hearing and Seeing – Giving a Robot Intelligent Sensors
21
Section 4: Taking Robotics Further

Extending to machine learning

Some of the smartest sounding types of robotics are those involved in machine learning. The code used throughout this book has not used machine learning and is instead used well-known algorithms. The Proportional Integral Derivative (PID) controller you used in this book is a system that makes adjustments to read a value, but it is not machine learning. However, optimizing PID values might come from a machine learning algorithm. We used Haar Cascade models to detect faces; this was also not machine learning, though OpenCV contributors probably used a machine learning system to generate these cascades.

Machine learning tends to be great at optimizing tasks and discovering and matching patterns, but poor at making fully formed intelligent-seeming behavior.

The basic overall idea of many machine learning systems involves having a set of starting examples, with some information on which are matches and which are not. The machine is expected to determine...