Artificial Vision and Language Processing for Robotics电子书

Preface

About the Book

About the Authors

Objectives

Audience

Approach

Minimum Hardware Requirements

Software Requirements

Conventions

Installation and Setup

Additional Resources

Chapter 1

Fundamentals of Robotics

Introduction

History of Robotics

Artificial Intelligence

Natural Language Processing

Computer Vision

Types of Robots

Industrial Robots

Service Robots

Hardware and Software of Robots

Robot Positioning

Exercise 1: Computing a Robot’s Position

How to Work with Robots

Exercise 2: Computing the Distance Traveled by a Wheel with Python

Exercise 3: Computing Final Position with Python

Activity 1: Robot Positioning Using Odometry with Python

Summary

Chapter 2

Introduction to Computer Vision

Introduction

Basic Algorithms in Computer Vision

Image Terminology

OpenCV

Basic Image Processing Algorithms

Thresholding

Exercise 4: Applying Various Thresholds to an Image

Morphological Transformations

Exercise 5: Applying the Various Morphological Transformations to an Image

Blurring (Smoothing)

Exercise 6: Applying the Various Blurring Methods to an Image

Exercise 7: Loading an Image and Applying the Learned Methods

Introduction to Machine Learning

Decision Trees and Boosting Algorithms

Bagging:

Boosting

Exercise 8: Predicting Numbers Using the Decision Tree, Random Forest, and AdaBoost Algorithms

Artificial Neural Networks (ANNs)

Exercise 9: Building Your First Neural Network

Activity 2: Classify 10 Types of Clothes from the Fashion-MNIST Database

Summary

Chapter 3

Fundamentals of Natural Language Processing

Introduction

Natural Language Processing

Parts of NLP

Levels of NLP

NLP in Python

Natural Language Toolkit (NLTK)

Exercise 10: Introduction to NLTK

spaCy

Exercise 11: Introduction to spaCy

Topic Modeling

Term Frequency – Inverse Document Frequency (TF-IDF)

Latent Semantic Analysis (LSA)

Exercise 12: Topic Modeling in Python

Activity 3: Process a Corpus

Language Modeling

Introduction to Language Models

The Bigram Model

N-gram Model

Calculating Probabilities

Exercise 13: Create a Bigram Model

Summary

Chapter 4

Neural Networks with NLP

Introduction

Recurrent Neural Networks

Introduction to Recurrent Neural Networks (RNN)

Inside Recurrent Neural Networks

RNN architectures

Long-Dependency Problem

Exercise 14: Predict House Prices with an RNN

Long Short-Term Memory

Exercise 15: Predict the Next Solution of a Mathematical Function

Neural Language Models

Introduction to Neural Language Models

RNN Language Model

Exercise 16: Encoding a Small Corpus

The Input Dimensions of RNNs

Activity 4: Predict the Next Character in a Sequence

Summary

Chapter 5

Convolutional Neural Networks for Computer Vision

Introduction

Fundamentals of CNNs

Building Your First CNN

Exercise 17: Building a CNN

Improving Your Model - Data Augmentation

Exercise 18: Improving Models Using Data Augmentation

Activity 5: Making Use of Data Augmentation to Classify correctly Images of Flowers

State-of-the-Art Models - Transfer Learning

Exercise 19: Classifying €5 and €20 Bills Using Transfer Learning with Very Little Data

Summary

Chapter 6

Robot Operating System (ROS)

Introduction

ROS Concepts

ROS Commands

Installation and Configuration

Catkin Workspaces and Packages

Publishers and Subscribers

Exercise 20: Publishing and Subscribing

Exercise 21: Publishers and Subscribers

Simulators

Exercise 22: The Turtlebot configuration

Exercise 23: Simulators and Sensors

Activity 6: Simulators and Sensors

Summary

Chapter 7

Build a Text-Based Dialogue System (Chatbot)

Introduction

Word Representation in Vector Space

Word Embeddings

Cosine Similarity

Word2Vec

Problems with Word2Vec

Gensim

Exercise 24: Creation of a Word Embedding

Global Vectors (GloVe)

Exercise 25: Using a Pretrained GloVe to See the Distribution of Words in a Plane

Dialogue Systems

Tools for Developing Chatbots

Types of Conversational Agents

Classification by Input-Output Data Type

Classification by System Knowledge

Creation of a Text-Based Dialogue System

Exercise 26: Create Your First Conversational Agent

Activity 7: Create a Conversational Agent to Control a Robot

Summary

Chapter 8

Object Recognition to Guide a Robot Using CNNs

Introduction

Multiple Object Recognition and Detection

Exercise 24: Building Your First Multiple Object Detection and Recognition Algorithm

ImageAI

Multiple Object Recognition and Detection in Video

Activity 8: Multiple Object Detection and Recognition in Video

Summary

Chapter 9

Computer Vision for Robotics

Introduction

Darknet

Basic Installation of Darknet

YOLO

First Steps in Image Classification with YOLO

YOLO on a Webcam

Exercise 28: Programming with YOLO

ROS Integration

Exercise 29: ROS and YOLO Integration

Activity 9: A Robotic Security Guard

Summary

Appendix

Chapter 1: Fundamentals of Robotics

Activity 1: Robot Positioning Using Odometry with Python

Chapter 2: Introduction to Computer Vision

Activity 2: Classify 10 Types of Clothes from the Fashion-MNIST Data

Chapter 3: Fundamentals of Natural Language Processing

Activity 3: Process a Corpus

Chapter 4: Neural Networks with NLP

Activity 4: Predict the Next Character in a Sequence

Chapter 5: Convolutional Neural Networks for Computer Vision

Activity 5: Making Use of Data Augmentation to Classify correctly Images of Flowers

Chapter 6: Robot Operating System (ROS)

Activity 6: Simulators and Sensor

Chapter 7: Build a Text-Based Dialogue System (Chatbot)

Activity 7: Create a Conversational Agent to Control a Robot

Chapter 8: Object Recognition to Guide a Robot Using CNNs

Activity 8: Multiple Object Detection and Recognition in Video

Chapter 9: Computer Vision for Robotics

Activity 9: A Robotic Security Guard