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Embedded Robotics: From Mobile Robots to Autonomous Vehicles with Raspberry Pi and Arduino 4th ed. 2022 [Pehme köide]

  • Formaat: Paperback / softback, 519 pages, kõrgus x laius: 242x170 mm, kaal: 909 g, 361 Illustrations, color; 93 Illustrations, black and white; XIII, 519 p. 454 illus., 361 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 24-Mar-2022
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811608032
  • ISBN-13: 9789811608032
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Embedded Robotics: From Mobile Robots to Autonomous Vehicles with Raspberry Pi and Arduino 4th ed. 2022Suurem pilt
  • Formaat: Paperback / softback, 519 pages, kõrgus x laius: 242x170 mm, kaal: 909 g, 361 Illustrations, color; 93 Illustrations, black and white; XIII, 519 p. 454 illus., 361 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 24-Mar-2022
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811608032
  • ISBN-13: 9789811608032
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811608032.html
Märksõnad:
This textbook presents a unique examination of mobile robots and autonomous vehicles using embedded systems, from introductory to advanced level. It is structured in four parts, dealing with Embedded Systems (processors, sensors, actuators, control, multitasking and communication), Robot Hardware (driving and walking robots, autonomous boats and planes, as well as robot manipulators), Robot Software (localization, navigation, image processing and automotive systems), and Artificial Intelligence (neural networks, genetic algorithms and deep learning). The book is organized for ease of use, with numerous figures, photographs, and worked example programs. The book is written as a text for courses in computer science, computer engineering, IT, electronics engineering, and mechatronics, as well as a guide for robot hobbyists and researchers. In this 4th edition the text has been thoroughly updated and extended. It now uses the Raspberry Pi and Arduino embedded processors for all mobile robot systems.
Part I: Embedded Systems
1 Robots and Controllers
3(14)
1.1 Mobile Robots
4(4)
1.2 Embedded Controllers
8(2)
1.3 Robot Design
10(2)
1.4 Operating System
12(2)
1.5 Simulation
14(1)
1.6 Tasks
15(2)
2 Central Processing Unit
17(36)
2.1 Logic Gates
18(5)
2.2 Function Units
23(6)
2.3 Registers and Memory
29(3)
2.4 Retro
32(2)
2.5 Arithmetic Logic Unit
34(3)
2.6 Control Unit
37(1)
2.7 Central Processing Unit
38(11)
2.8 Structured Design
49(3)
2.9 Tasks
52(1)
3 Arduino
53(14)
3.1 Arduino Hardware
54(3)
3.2 Arduino Programming
57(1)
3.3 Arduino Interfacing
58(4)
3.4 Arduino Communication
62(1)
3.5 Beyond Arduino
63(2)
3.6 Tasks
65(2)
4 Raspberry Pi
67(18)
4.1 Raspberry Pi Operating System and Setup
67(4)
4.2 Raspberry Pi Tools and Programming
71(6)
4.3 Raspberry Pi Input/Output Lines
77(3)
4.4 Raspberry Pi Communication
80(1)
4.5 Integration Development Environments
81(2)
4.6 Tasks
83(2)
5 Sensors and Interfaces
85(26)
5.1 Sensor Categories
85(2)
5.2 Synchronous Serial and I2C Interfaces
87(2)
5.3 Binary Sensors
89(1)
5.4 Shaft Encoders
89(2)
5.5 A/D Converters
91(1)
5.6 Position Sensitive Devices-Sonar, Infrared, Laser
92(4)
5.7 Lidar Sensors
96(2)
5.8 Orientation Sensors
98(1)
5.9 Inertial Measurement Units
99(2)
5.10 Global Navigation Satellite Systems
101(3)
5.11 Digital Image Sensors
104(5)
5.12 Tasks
109(2)
6 Actuators
111(12)
6.1 DC Motors
111(4)
6.2 H-Bridge
115(1)
6.3 Pulse Width Modulation
116(2)
6.4 Stepper Motors
118(1)
6.5 Servos
119(2)
6.6 Tasks
121(2)
7 Control
123(24)
7.1 On-Off Control
123(6)
7.2 PID Control
129(4)
7.3 Derivative Controller
133(3)
7.4 Velocity Control and Position Control
136(3)
7.5 Multiple Motors-Driving Straight
139(3)
7.6 V-Omega Interface
142(4)
7.7 Tasks
146(1)
8 Multitasking
147(14)
8.1 Preemptive Multithreading
148(1)
8.2 Synchronization
149(5)
8.3 Scheduling
154(3)
8.4 Interrupts and Timer-Activated Tasks
157(3)
8.5 Tasks
160(1)
9 Communication
161(14)
9.1 Communication Channels
161(2)
9.2 File Transfer and Remote Access
163(3)
9.3 Radio Library
166(1)
9.4 Robot to Robot Communication
166(2)
9.5 Robot to PC Communication
168(3)
9.6 Tasks
171(4)
Part II: Robot Hardware
10 Driving Robots
175(34)
10.1 Single Wheel Drive
175(1)
10.2 Differential Drive
176(13)
10.3 Tracked Robots
189(1)
10.4 Synchro-Drive
190(2)
10.5 Ackermann Steering
192(4)
10.6 Omni-Directional Robots
196(6)
10.7 Drive Kinematics
202(6)
10.8 Tasks
208(1)
11 Walking Robots
209(24)
11.1 Balancing Robots
210(5)
11.2 Six-Legged Robots
215(2)
11.3 Biped Robots
217(6)
11.4 Static Balance
223(3)
11.5 Dynamic Balance
226(6)
11.6 Tasks
232(1)
12 Autonomous Boats and Planes
233(20)
12.1 Autonomous Boats
233(3)
12.2 Autonomous Underwater Vehicles
236(12)
12.3 Unmanned Aerial Vehicles (UAVs)
248(3)
12.4 Tasks
251(2)
13 Robot Manipulators
253(20)
13.1 Homogeneous Coordinates
254(2)
13.2 Manipulator Kinematics
256(5)
13.3 Manipulator Simulation
261(3)
13.4 Teaching and Programming
264(1)
13.5 Industrial Manipulators
265(4)
13.6 Tasks
269(4)
Part III: Robot Software
14 Localization and Navigation
273(38)
14.1 Localization
273(4)
14.2 Environment Representation
277(3)
14.3 Quadtree
280(4)
14.4 Visibility Graph
284(2)
14.5 Voronoi Diagram and Brushfire Algorithm
286(3)
14.6 Potential Field Method
289(2)
14.7 Wandering Standpoint Algorithm
291(1)
14.8 Bug Algorithm Family
292(3)
14.9 Dijkstra's Algorithm
295(4)
14.10 A* Algorithm
299(2)
14.11 Probabilistic Localization
301(5)
14.12 SLAM
306(3)
14.13 Tasks
309(2)
15 Maze Navigation
311(14)
15.1 Micro Mouse Contest
311(3)
15.2 Maze Exploration Algorithms
314(7)
15.3 Simulated Versus Real Maze Program
321(3)
15.4 Tasks
324(1)
16 Image Processing
325(32)
16.1 Camera Interface
325(2)
16.2 Image File Formats
327(3)
16.3 Edge Detection
330(5)
16.4 Motion Detection
335(3)
16.5 Color Spaces
338(4)
16.6 RBG-to-HSI Conversion
342(3)
16.7 Color Object Detection
345(4)
16.8 Image Segmentation
349(3)
16.9 Image Coordinates Versus World Coordinates
352(3)
16.10 Tasks
355(2)
17 Automotive Systems
357(46)
17.1 Autonomous Automobiles
358(3)
17.2 Drive-By-Wire and Safety Systems
361(3)
17.3 Computer Vision for Autonomous Driving
364(2)
17.4 OpenCV and KITTI
366(1)
17.5 ROS
367(3)
17.6 Carla Simulator
370(2)
17.7 Lane Detection
372(8)
17.8 Vehicle Recognition and Tracking
380(4)
17.9 Automatic Parking
384(4)
17.10 Autonomous Formula-SAE
388(4)
17.11 Autonomous Shuttle Bus
392(7)
17.12 Tasks
399(4)
Part IV: Artificial Intelligence
18 AI Concepts
403(18)
18.1 Software Architecture
403(2)
18.2 Behavior-Based Systems
405(1)
18.3 Behavior Framework
406(5)
18.4 Behavior-Based Applications
411(8)
18.5 Tasks
419(2)
19 Neural Networks
421(18)
19.1 Neural Network Principles
421(2)
19.2 Feed-Forward Networks
423(5)
19.3 Backpropagation
428(5)
19.4 Neural Network Examples
433(2)
19.5 Neural Robot Control
435(2)
19.6 Tasks
437(2)
20 Genetic Algorithms
439(30)
20.1 Genetic Algorithm Principles
440(2)
20.2 Genetic Operators
442(3)
20.3 Evolution Example
445(3)
20.4 Implementing Genetic Algorithms
448(5)
20.5 Genetic Robot Control
453(2)
20.6 Starman
455(2)
20.7 Evolving Walking Gaits
457(10)
20.8 Tasks
467(2)
21 Deep Learning
469(16)
21.1 TensorFlow and Caffe
471(1)
21.2 Carolo-Cup Competition
472(2)
21.3 Traffic Sign Recognition
474(4)
21.4 End-To-End Learning for Autonomous Driving
478(6)
21.5 Tasks
484(1)
22 Outlook
485(4)
Appendix A: RoBIOS Library 489(8)
Appendix B: EyeBot-IO7 Interface 497(6)
Appendix C: Hardware Description Table 503(4)
Appendix D: Robot Programming Projects 507
Thomas Bräunl is Professor at The University of Western Australia, Perth, where he directs the Robotics & Automation Lab as well as the Renewable Energy Vehicle Lab. He has developed numerous robotics systems, including the EyeBot robot family and the EyeSim simulation system. Professor Bräunl worked on Autonomous Driving Systems with Mercedes-Benz and on Electric Vehicle Systems with BMW. He holds a Diploma from Univ. Kaiserslautern, an M.S. degree from the University of Southern California, Los Angeles, and a Ph.D. and Habilitation from Univ. Stuttgart.