Engineering Autonomous Vehicles and Robots

Engineering Autonomous Vehicles and Robots
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Описание книги

Offers a step-by-step guide to building autonomous vehicles and robots, with source code and accompanying videos The first book of its kind on the detailed steps for creating an autonomous vehicle or robot, this book provides an overview of the technology and introduction of the key elements involved in developing autonomous vehicles, and offers an excellent introduction to the basics for someone new to the topic of autonomous vehicles and the innovative, modular-based engineering approach called DragonFly. Engineering Autonomous Vehicles and Robots: The DragonFly Modular-based Approach covers everything that technical professionals need to know about: CAN bus, chassis, sonars, radars, GNSS, computer vision, localization, perception, motion planning, and more. Particularly, it covers Computer Vision for active perception and localization, as well as mapping and motion planning. The book offers several case studies on the building of an autonomous passenger pod, bus, and vending robot. It features a large amount of supplementary material, including the standard protocol and sample codes for chassis, sonar, and radar. GPSD protocol/NMEA protocol and GPS deployment methods are also provided. Most importantly, readers will learn the philosophy behind the DragonFly modular-based design approach, which empowers readers to design and build their own autonomous vehicles and robots with flexibility and affordability. Offers progressive guidance on building autonomous vehicles and robots Provides detailed steps and codes to create an autonomous machine, at affordable cost, and with a modular approach Written by one of the pioneers in the field building autonomous vehicles Includes case studies, source code, and state-of-the art research results Accompanied by a website with supplementary material, including sample code for chassis/sonar/radar; GPS deployment methods; Vision Calibration methods Engineering Autonomous Vehicles and Robots is an excellent book for students, researchers, and practitioners in the field of autonomous vehicles and robots.

Оглавление

Shaoshan Liu. Engineering Autonomous Vehicles and Robots

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Engineering Autonomous Vehicles and Robots. The DragonFly Modular-based Approach

1 Affordable and Reliable Autonomous Driving Through Modular Design. 1.1 Introduction

1.2 High Cost of Autonomous Driving Technologies

1.2.1 Sensing

1.2.2 HD Map Creation and Maintenance

1.2.3 Computing Systems

1.3 Achieving Affordability and Reliability

1.3.1 Sensor Fusion

1.3.2 Modular Design

1.3.3 Extending Existing Digital Maps

1.4 Modular Design

1.4.1 Communication System

1.4.2 Chassis

1.4.3 mmWave Radar and Sonar for Passive Perception

1.4.4 GNSS for Localization

1.4.5 Computer Vision for Active Perception and Localization

1.4.6 Planning and Control

1.4.7 Mapping

1.5 The Rest of the Book

1.6 Open Source Projects Used in this Book

References

2 In-Vehicle Communication Systems. 2.1 Introduction

2.2 CAN

2.3 FlexRay

2.3.1 FlexRay Topology

2.3.2 The FlexRay Communication Protocol

2.4 CANopen

2.4.1 Object Dictionary

2.4.2 Profile Family

2.4.3 Data Transmission and Network Management

2.4.4 Communication Models

2.4.5 CANopenNode

References

3 Chassis Technologies for Autonomous Robots and Vehicles. 3.1 Introduction

3.2 Throttle-by-Wire

3.3 Brake-by-Wire

3.4 Steer-by-Wire

3.5 Open Source Car Control

3.5.1 OSCC APIs

3.5.2 Hardware

3.5.3 Firmware

3.6 OpenCaret

3.6.1 OSCC Throttle

3.6.2 OSCC Brake

3.6.3 OSCC Steering

3.7 PerceptIn Chassis Software Adaptation Layer

References

4 Passive Perception with Sonar and Millimeter Wave Radar. 4.1 Introduction

4.2 The Fundamentals of mmWave Radar

4.2.1 Range Measurement

4.2.2 Velocity Measurement

4.2.3 Angle Detection

4.3 mmWave Radar Deployment

4.4 Sonar Deployment

References

5 Localization with Real-Time Kinematic Global Navigation Satellite System. 5.1 Introduction

5.2 GNSS Technology Overview

5.3 RTK GNSS

5.4 RTK-GNSS NtripCaster Setup Steps

5.4.1 Set up NtripCaster

5.4.2 Start NtripCaster

5.5 Setting Up NtripServer and NtripClient on Raspberry Pi. 5.5.1 Install the Raspberry Pi System

5.5.2 Run RTKLIB-str2str on the Raspberry Pi. 5.5.2.1 Running NtripServer on the Base Station Side

5.5.2.2 Running NtripClient on the GNSS Rover

5.6 Setting Up a Base Station and a GNSS Rover. 5.6.1 Base Station Hardware Setup

5.6.2 Base Station Software Setup

5.6.3 GNSS Rover Setup. 5.6.3.1 Rover Hardware Setup

5.6.3.2 Rover Software Setup

5.7 FreeWave Radio Basic Configuration

References

6 Computer Vision for Perception and Localization. 6.1 Introduction

6.2 Building Computer Vision Hardware

6.2.1 Seven Layers of Technologies

6.2.2 Hardware Synchronization

6.2.3 Computing

6.3 Calibration

6.3.1 Intrinsic Parameters

6.3.2 Extrinsic Parameters

6.3.3 Kalibr

6.3.3.1 Calibration Target

6.3.3.2 Multiple Camera Calibration

6.3.3.3 Camera IMU Calibration

6.3.3.4 Multi-IMU and IMU Intrinsic Calibration

6.4 Localization with Computer Vision

6.4.1 VSLAM Overview

6.4.2 ORB-SLAM2

6.4.2.1 Prerequisites

6.4.2.2 Building the ORB-SLAM2 Library

6.4.2.3 Running Stereo Datasets

6.5 Perception with Computer Vision

6.5.1 ELAS for Stereo Depth Perception

6.5.2 Mask R-CNN for Object Instance Segmentation

6.6 The DragonFly Computer Vision Module

6.6.1 DragonFly Localization Interface

6.6.2 DragonFly Perception Interface

6.6.3 DragonFly+

References

7 Planning and Control. 7.1 Introduction

7.2 Route Planning

7.2.1 Weighted Directed Graph

7.2.2 Dijkstra's Algorithm

7.2.3 A* Algorithm

7.3 Behavioral Planning

7.3.1 Markov Decision Process

7.3.2 Value Iteration Algorithm

7.3.3 Partially Observable Markov Decision Process (POMDP)

7.3.4 Solving POMDP

7.4 Motion Planning

7.4.1 Rapidly Exploring Random Tree

7.4.2 RRT*

7.5 Feedback Control

7.5.1 Proportional–Integral–Derivative Controller

7.5.2 Model Predictive Control

7.6 Iterative EM Plannning System in Apollo

7.6.1 Terminologies. 7.6.1.1 Path and Trajectory

7.6.1.2 SL Coordinate System and Reference Line

7.6.1.3 ST Graph

7.6.2 Iterative EM Planning Algorithm

7.6.2.1 Traffic Decider

7.6.2.2 QP Path and QP Speed

7.7 PerceptIn's Planning and Control Framework

References

8 Mapping. 8.1 Introduction

8.2 Digital Maps

8.2.1 Open Street Map

8.2.1.1 OSM Data Structures

8.2.1.2 OSM Software Stack

8.2.2 Java OpenStreetMap Editor

8.2.2.1 Adding a Node or a Way

8.2.2.2 Adding Tags

8.2.2.3 Uploading to OSM

8.2.3 Nominatim

8.2.3.1 Nominatim Architecture

8.2.3.2 Place Ranking in Nominatim

8.3 High-Definition Maps

8.3.1 Characteristics of HD Maps. 8.3.1.1 High Precision

8.3.1.2 Rich Geometric Information and Semantics

8.3.1.3 Fresh Data

8.3.2 Layers of HD Maps

8.3.2.1 2D Orthographic Reflectivity Map

8.3.2.2 Digital Elevation Model

8.3.2.3 Lane/Road Model

8.3.2.4 Stationary Map

8.3.3 HD Map Creation

8.3.3.1 Data Collection

8.3.3.2 Offline Generation of HD Maps

8.3.3.3 Quality Control and Validation

8.3.3.4 Update and Maintenance

8.3.3.5 Problems of HD Maps

8.4 PerceptIn's π-Map

8.4.1 Topological Map

8.4.2 π-Map Creation

References

9 Building the DragonFly Pod and Bus. 9.1 Introduction

9.2 Chassis Hardware Specifications

9.3 Sensor Configurations

9.4 Software Architecture

9.5 Mechanism

9.6 Data Structures

9.6.1 Common Data Structures

Common.proto

Geometry.proto

9.6.2 Chassis Data

Chassis.proto

9.6.3 Localization Data

Localization.proto

9.6.4 Perception Data

Perception.proto

9.6.5 Planning Data

Decision.proto

Planning.proto

9.7 User Interface

References

10 Enabling Commercial Autonomous Space Robotic Explorers. 10.1 Introduction

10.2 Destination Mars

10.3 Mars Explorer Autonomy

10.3.1 Localization

10.3.2 Perception

10.3.3 Path Planning

10.3.4 The Curiosity Rover and Mars 2020 Explorer

10.4 Challenge: Onboard Computing Capability

10.5 Conclusion

References

11 Edge Computing for Autonomous Vehicles. 11.1 Introduction

11.2 Benchmarks

11.3 Computing System Architectures

11.4 Runtime

11.5 Middleware

11.6 Case Studies

References

12 Innovations on the Vehicle-to-Everything Infrastructure. 12.1 Introduction

12.2 Evolution of V2X Technology

12.3 Cooperative Autonomous Driving

12.4 Challenges

References

13 Vehicular Edge Security. 13.1 Introduction

13.2 Sensor Security

13.3 Operating System Security

13.4 Control System Security

13.5 V2X Security

13.6 Security for Edge Computing

References

Index

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Отрывок из книги

Shaoshan Liu

Fremont, CA

.....

Creating and maintaining HD maps is another important component of deployment costs. Crowd-sourcing the data for creating HD maps has been proposed. However, this would require vehicles with LiDAR units, and we have already seen that LiDARs are extremely expensive and thus not ready for large-scale deployment. On the other hand, crowd-sourcing visual data is a very practical solution as many cars today are already equipped with cameras.

Hence, instead of building HD maps from scratch, our philosophy is to enhance existing digital maps with visual information to achieve decimeter-level accuracy. These are called HPVMs. To effectively help with vehicle localization, HPVMs consists of multiple layers:

.....

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