Robotic Vehicles Systems And Technology

This book introduces the technological innovations of robotic vehicles. It presents the concepts required for self-driving cars on the road. Besides, readers can gain invaluable knowledge in the construction, programming, and control of the six-legged robot. The book also presents the controllers and aerodynamics of several different types of rotorcrafts. It includes the simulation and flight of the various kinds of rotor-propelled air vehicles under each of their different aerodynamics environment. The book is suitable for academia, educators, students, and researchers who are interested in autonomous vehicles, robotics, and rotor-propelled vehicles.

Methods of contro1151 Mechanical master-slave telemanipulators 151 Powered telemanipulators 152 Servo control of unilateral telemanipulators 152 Bilateral servo manipulators 155 Special characteristics of teleoperators 158 Design criteria for teleoperators 159 Vehicles and transporters 160 Applications of teleoperators 161 Remote handling of radioactive materials 161 Remote handling of explosive and toxic materials 161 Telemanipulation of heavy objects 163 Underwater teleoperation 163 Teleoperation in space and planetary exploration 164 Telemanipulators for the disabled 164 Computer assisted teleoperation 166 Bibliographic notes 170 Chapter 9: Mobile robots 171 Introduction 171 Land surface robots 171 Arrangements of wheels and tracks 171 Unusual wheel and track arrangements 172 Navigation for land vehicles 174 Teleoperation 174 Dead reckoning 175 Inertial navigation 175 Tracking from a fixed base; beacons 175 Satellite navigation 175 Map matching 175 Wall following 176 Route planning 176 Control and communication 176 Sensors for mobile robots 177 Body orientation and angular rates 1 77 Body position, speed and acceleration 177 Terrain scanning 178 Types and applications of mobile robots 179 Education and research 179 Remote handling 183 Military mobile robots 183 Fire-fighting and rescue 187 Construction 188 Mining 188 Planetary exploration 188 Legged robots 188 Comparison of legs and wheels 189 Leg number and arrangement 189 Leg number 189 Leg disposition 190 Relative leg length 190 Leg construction 190 Control 191 Climbing robots 195 Robot submersibles 196 Uses of submersible robots 199 Robots in air and space 201 Space 202 Bibliographic notes 204 Chapter 10: Automated guided vehicles 205

This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies.

This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Nonlinear Control of Vehicles and Robots develops a unified approach to the dynamic modeling of robots in terrestrial, aerial and marine environments. The main classes of nonlinear systems and stability methods are summarized and basic nonlinear control methods, useful in manipulator and vehicle control, are presented. Formation control of ground robots and ships is discussed. The book also deals with the modeling and control of robotic systems in the presence of non-smooth nonlinearities. Robust adaptive tracking control of robotic systems with unknown payload and friction in the presence of uncertainties is treated. Theoretical and practical aspects of the control algorithms under discussion are detailed. Examples are included throughout the book allowing the reader to apply the control and modeling techniques in their own research and development work. Some of these examples demonstrate state estimation based on the use of advanced sensors as part of the control system.

Learn how to program robotic vehicles with ardupilot libraries and pixhawk autopilot, both of which are open source technologies with a global scope. This book is focused on quadcopters but the knowledge is easily extendable to three-dimensional vehicles such as drones, submarines, and rovers. Pixhawk and the ardupilot libraries have grown dramatically in popularity due to the fact that the hardware and software offer a real-time task scheduler, huge data processing capabilities, interconnectivity, low power consumption, and a global developer support. This book shows you how take your robotic programming skills to the next level. From hardware to software, Advanced Robotic Vehicles Programming links theory with practice in the development of unmanned vehicles. By the end of this book, you’ll learn the pixhawk software and ardupilot libraries to develop your own autonomous vehicles. What You'll Learn Model and implement elementary controls in any unmanned vehicle Select hardware and software components during the design process of an unmanned vehicle Use other compatible hardware and software development packages Understand popular scientific and technical nomenclature in the field Identify relevant complexities and processes for the operation of an unmanned vehicle Who This Book Is For Undergraduate and graduate students, researchers, makers, hobbyists, and those who want to go beyond basic programming of an Arduino for any kind of robotic vehicle.

This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Workshop held on Maui, Hawaii, on August 14, 1994.

Why the United States lags behind other industrialized countries in sharing the benefits of innovation with workers and how we can remedy the problem. The United States has too many low-quality, low-wage jobs. Every country has its share, but those in the United States are especially poorly paid and often without benefits. Meanwhile, overall productivity increases steadily and new technology has transformed large parts of the economy, enhancing the skills and paychecks of higher paid knowledge workers. What’s wrong with this picture? Why have so many workers benefited so little from decades of growth? The Work of the Future shows that technology is neither the problem nor the solution. We can build better jobs if we create institutions that leverage technological innovation and also support workers though long cycles of technological transformation. Building on findings from the multiyear MIT Task Force on the Work of the Future, the book argues that we must foster institutional innovations that complement technological change. Skills programs that emphasize work-based and hybrid learning (in person and online), for example, empower workers to become and remain productive in a continuously evolving workplace. Industries fueled by new technology that augments workers can supply good jobs, and federal investment in R&D can help make these industries worker-friendly. We must act to ensure that the labor market of the future offers benefits, opportunity, and a measure of economic security to all.