Book Image

Artificial Intelligence for Robotics

By : Francis X. Govers
Book Image

Artificial Intelligence for Robotics

By: Francis X. Govers

Overview of this book

Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.
Table of Contents (13 chapters)

Chapter 4, Object Recognition Using Neural Networks and Supervised Learning

  1. This is an exercise for the student. You should see different curves develop as the activation function is changed. Some will not produce an answer at all (which look like random results – the curve stays at the same level as no learning is taking place). Some will learn faster or slower.
  2. Refer to Figure 3 in the chapter. The artificial neuron has a number of inputs, a set of weights, one for each input, a bias, an activation, and a set of outputs.
  3. Both have multiple inputs and multiple outputs, and accept inputs, perform some processing, and then make an output. Both use some sort of activation to determine when to “fire” or produce an output.
  1. The natural neuron is an analog device that can handle many levels or degrees of inputs, with no simple on/off binary representations like...