The basic principle of robotics and AI
AI applied to robotics development requires a different set of skills from you, the robot designer or developer. You may have made robots before. You probably have a quadcopter or a 3D printer (which is, in fact, a robot). The familiar world of Proportional-Integral-Derivative (PID) controllers, sensor loops, and state machines is augmented by Artificial Neural Networks (ANNs), expert systems, genetic algorithms, and searching path planners. We want a robot that does not just react to its environment as a reflex action but has goals and intent – and can learn and adapt to the environment and is taught or trained rather than programmed. Some of the problems we can solve this way would be difficult, intractable, or impossible otherwise.
What we are going to do in this book is introduce a problem – picking up toys in a playroom – that we will use as our example throughout the book as we learn a series of techniques for applying AI to our robot. It is important to understand that, in this book, the journey is far more important than the destination. At the end of the book, you should have gained some important skills with broad applicability, not just learned how to pick up toys.
What we are going to do is first provide some tools and background to match the infrastructure that was used to develop the examples in the book. This is both to provide an even playing field and to not assume any practical knowledge on your part. To execute some of the advanced neural networks that we are going to build, we will use the GPUs in the Jetson.
In the rest of this chapter, we will discuss some basics about robotics and AI, and then proceed to develop two important tools that we will use in all of the examples in the rest of the book. We will introduce the concept of soft real-time control, and then provide a framework, or model, for creating autonomy for our robot called the Observe-Orient-Decide-Act (OODA) loop.
