▶Book Description
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.
▶What You Will Learn
⦁ Get started with robotics and artificial intelligence
⦁ Apply simulation techniques to give your robot an artificial personality
⦁ Understand object recognition using neural networks and supervised learning techniques
⦁ Pick up objects using genetic algorithms for manipulation
⦁ Teach your robot to listen using NLP via an expert system
⦁ Use machine learning and computer vision to teach your robot how to avoid obstacles
⦁ Understand path planning, decision trees, and search algorithms in order to enhance your robot
▶Key Features
⦁ Leverage fundamentals of AI and robotics
⦁ Work through use cases to implement various machine learning algorithms
⦁ Explore Natural Language Processing (NLP) concepts for efficient decision making in robots
▶Who This Book Is For
This book is designed for intermediate to advanced robotics researchers, professionals, and hobbyists, as well as students who have worked past the basics of robotics and are looking for the next step in their education and skill set.
Readers should be familiar with Python and the Robotics Operating System (ROS), as well as Linux. Advanced math is most definitely not required to get a lot out of this book.
▶What this book covers
⦁ Chapter 1, Foundation for Robotics and AI, introduces artificial intelligence (AI) and covers the basics of robotics as applied in this book. The chapter also introduces the AI framework used, which is the Observe-Orient-Decide-Act (OODA) model, and soft real-time control.
⦁ Chapter 2, Setting Up Your Robot, covers the robot architecture, ROS, and setting up the software and hardware, including the construction of the robot example for the book.
⦁ Chapter 3, A Concept for a Practical Robot Design Process, introduces a simplified systems approach to robot design that combines use cases (from systems engineering) and storyboards (from Agile development) to give the reader a structure and a process to use when solving problems with robots and AI.
⦁ Chapter 4, Object Recognition Using Neural Networks and Supervised Learning, teaches how to build an artificial neural network. Readers will learn the basics of image recognition as well as the training and evaluation of neural networks using Keras and Python.
⦁ Chapter 5, Picking Up the Toys, introduces techniques that allow the robot to learn for itself how to user its robot arm. The key technique is to have a mechanism for the robot to score how well it does. We explore reinforcement learning and dive into Genetic Algorithms.
⦁ Chapter 6, Teaching the Robot to Listen, We develop on top of a voice-based command system, a type of digital assistant that uses AI techniques to understand words and divine the intent of the speaker. Basic concepts of speech recognition and natural language processing are introduced, such as context, knowledge bases, intent recognition, and sentence reconstruction. We teach the robot to both tell and understand knock-knock jokes.
⦁ Chapter 7, Avoiding the Stairs, helps the readers understand robot navigation, including SLAM. It will help you navigate the robot using a combination of two techniques: Floor Finding for obstacle avoidance, and Neural Network Image recognition for learned navigation without a map.
⦁ Chapter 8, Putting Things Away, covers path planning, decision trees, classification techniques, wave front, the A* (A star) and D* (D star) algorithms, and node-based planners.
⦁ Chapter 9, Giving the Robot an Artificial Personality, describes simulation and Monte Carlo modeling, the Robot Emotion Engine, the Human Emotion Model, and integrating personality rules into a chatbot-based conversation engine.
⦁ Chapter 10, Conclusions and Remarks, has some words about the future of AI and robotics, as well as advice about robotics as a career.
