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E-raamat: Artificial Intelligence in Wireless Robotics

  • Formaat: 354 pages
  • Ilmumisaeg: 01-Sep-2022
  • Kirjastus: River Publishers
  • Keel: eng
  • ISBN-13: 9781000796568
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Artificial Intelligence in Wireless RoboticsSuurem pilt
  • Formaat: 354 pages
  • Ilmumisaeg: 01-Sep-2022
  • Kirjastus: River Publishers
  • Keel: eng
  • ISBN-13: 9781000796568
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Robots, autonomous vehicles, unmanned aerial vehicles, and smart factory, will significantly change human living style in digital society. Artificial Intelligence in Wireless Robotics introduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems.

The technical contents of this book include fundamental knowledge in robotics, cyber-physical systems, artificial intelligence, statistical decision and Markov decision process, reinforcement learning, state estimation, localization, computer vision and multi-modal data fusion, robot planning, multi-agent systems, networked multi-agent systems, security and robustness of networked robots, and ultra-reliable and low-latency machine-to-machine networking. Examples and exercises are provided for easy and effective comprehension.

Engineers wishing to extend knowledge in the robotics, AI, and wireless communications, would be benefited from this book. In the meantime, the book is ready as a textbook for senior undergraduate students or first-year graduate students in electrical engineering, computer engineering, computer science, and general engineering students. The readers of this book shall have basic knowledge in undergraduate probability and linear algebra, and basic programming capability, in order to enjoy deep reading.
Preface xi
List of Figures
xv
List of Tables
xxv
List of Abbreviations
xxvii
1 Introduction to Artificial Intelligence and Robotics
1(24)
1.1 Common Sense Knowledge of AI, Cybernetics, and Robotics
1(5)
1.2 Intelligent Agents
6(8)
1.2.1 The Concept of Rationality
6(2)
1.2.2 System Dynamics
8(1)
1.2.3 Task Environments
9(3)
1.2.4 Robotics and Multi-Agent Systems
12(2)
1.3 Reasoning
14(11)
1.3.1 Constraint Satisfaction Problems
16(2)
1.3.2 Solving CSP by Search
18(6)
References
24(1)
2 Basic Search Algorithms
25(28)
2.1 Problem-Solving Agents
25(4)
2.2 Searching for Solutions
29(4)
2.3 Uniform Search
33(11)
2.3.1 Breadth-First Search
33(3)
2.3.2 Dynamic Programming
36(6)
2.3.3 Depth-first Search
42(2)
2.4 Informed Search
44(5)
2.5 Optimization
49(4)
2.5.1 Linear Programming
49(1)
2.5.2 Nonlinear Programming
50(1)
2.5.3 Convex Optimization
51(1)
References
52(1)
3 Machine Learning Basics
53(28)
3.1 Supervised Learning
55(12)
3.1.1 Regression
55(7)
3.1.2 Bayesian Classification
62(2)
3.1.3 KNN
64(2)
3.1.4 Support Vector Machine
66(1)
3.2 Unsupervised Learning
67(6)
3.2.1 if-Means Clustering
67(2)
3.2.2 EM Algorithms
69(1)
3.2.3 Principal Component Analysis
70(3)
3.3 Deep Neural Networks
73(3)
3.4 Data Preprocessing
76(5)
References
80(1)
4 Markov Decision Processes
81(46)
4.1 Statistical Decisions
81(14)
4.1.1 Mathematical Foundation
85(1)
4.1.2 Bayes Decision
86(6)
4.1.3 Radar Signal Detection
92(1)
4.1.4 Bayesian Sequential Decision
93(2)
4.2 Markov Decision Processes
95(8)
4.2.1 Mathematical Formulation of Markov Decision Process
96(3)
4.2.2 Optimal Policies
99(1)
4.2.3 Developing Solutions to Bellman Equation
100(3)
4.3 Decision Making and Planning: Dynamic Programming
103(6)
4.4 Application of MDP to Search A Mobile Target
109(4)
4.5 Multi-Armed Bandit Problem
113(14)
4.5.1 e-Greedy Algorithm
116(1)
4.5.2 Upper Confidence Bounds
116(2)
4.5.3 Thompson Sampling
118(8)
References
126(1)
5 Reinforcement Learning
127(36)
5.1 Fundamentals of Reinforcement Learning
128(10)
5.1.1 Revisit of Multi-Armed Bandit Problem
128(4)
5.1.2 Basics in Reinforcement Learning
132(1)
5.1.3 Reinforcement Learning Based on Markov Decision Process
133(3)
5.1.4 Bellman Optimality Principle
136(2)
5.2 Q-Learning
138(11)
5.2.1 Partially Observable States
138(2)
5.2.2 Q-Learning Algorithm
140(2)
5.2.3 Illustration of Q-Learning
142(7)
5.3 Model-Free Learning
149(14)
5.3.1 Monte Carlo Methods
150(3)
5.3.2 Temporal Difference Learning
153(5)
5.3.3 SARSA
158(1)
5.3.4 Relationship Between Q-Learning and TD-Learning
158(3)
References
161(2)
6 State Estimation
163(26)
6.1 Fundamentals of Estimation
163(10)
6.1.1 Linear Estimator from Observations
164(3)
6.1.2 Linear Prediction
167(1)
6.1.3 Bayesian Estimation
168(3)
6.1.4 Maximum Likelihood Estimation
171(2)
6.2 Recursive State Estimation
173(3)
6.3 Bayes Filters
176(3)
6.4 Gaussian Filters
179(10)
6.4.1 Kalman Filter
179(2)
6.4.2 Scalar Kalman Filter
181(5)
6.4.3 Extended Kalman Filter
186(2)
References
188(1)
7 Localization
189(26)
7.1 Localization By Sensor Network
190(8)
7.1.1 Time-of-Arrival Techniques
190(3)
7.1.2 Angle-of-Arrival Techniques
193(3)
7.1.3 Time-Difference-of-Arrivals Techniques
196(2)
7.2 Mobile Robot Localization
198(2)
7.3 Simultaneous Localization and Mapping
200(8)
7.3.1 Probabilistic SLAM
200(3)
7.3.2 SLAM with Extended Kalman Filter
203(2)
7.3.3 SLAM Assisted by Stereo Camera
205(3)
7.4 Network Localization and Navigation
208(7)
References
212(3)
8 Robot Planning
215(26)
8.1 Knowledge Representation and Classic Logic
215(10)
8.1.1 Bayesian Networks
217(7)
8.1.2 Semantic Representation
224(1)
8.2 Discrete Planning
225(3)
8.3 Planning and Navigation of An Autonomous Mobile Robot
228(13)
8.3.1 Illustrative Example for Planning and Navigation
229(1)
8.3.2 Reinforcement Learning Formulation
230(3)
8.3.3 Fixed Length Planning
233(1)
8.3.4 Conditional Exhaustive Planning
234(5)
References
239(2)
9 Multi-Modal Data Fusion
241(36)
9.1 Computer Vision
241(6)
9.1.1 Basics of Computer Vision
243(1)
9.1.2 Edge Detection
244(2)
9.1.3 Image Features and Object Recognition
246(1)
9.2 Multi-Modal Information Fusion Based on Visionary Functionalities
247(5)
9.3 Decision Trees
252(16)
9.3.1 Illustration of Decisions
252(3)
9.3.2 Formal Treatment
255(1)
9.3.3 Classification Trees
256(1)
9.3.4 Regression Trees
257(2)
9.3.5 Rules and Trees
259(1)
9.3.6 Localizing A Robot
259(3)
9.3.7 Reinforcement Learning with Decision Trees
262(6)
9.4 Federated Learning
268(9)
9.4.1 Federated Learning Basics
268(2)
9.4.2 Federated Learning Through Wireless Communications
270(1)
9.4.3 Federated Learning over Wireless Networks
271(2)
9.4.4 Federated Learning over Multiple Access Communications
273(2)
References
275(2)
10 Multi-Robot Systems
277(38)
10.1 Multi-Robot Task Allocation
278(9)
10.1.1 Optimal Allocation
278(3)
10.1.2 Multiple Traveling Salesmen Problem
281(1)
10.1.3 Factory Automation
282(5)
10.2 Wireless Communications and Networks
287(9)
10.2.1 Digital Communication Systems
288(4)
10.2.2 Computer Networks
292(2)
10.2.3 Multiple Access Communication
294(2)
10.3 Networked Multi-Robot Systems
296(19)
10.3.1 Connected Autonomous Vehicles in Manhattan Streets
296(10)
10.3.2 Networked Collaborative Multi-Robot Systems
306(7)
References
313(2)
Index 315(8)
About the Author 323
Kwang-Cheng Chen
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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