Artificial intelligence in wireless robotics

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...

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Bibliographic Details
Main Author: Chen, Kwang-Cheng, (Author)
Format: eBook
Language: English
Published: Gistrup, Denmark : River Publishers, [2020]
Series: River Publishers series in information science and technology.
Subjects:
Autonomous robots.
Wireless communication systems.
Artificial intelligence.
elektronické knihy
electronic books
ISBN: 1000793044
9781000793048
1523138955
9781523138951
8770221170
9788770221177
9781003337256
1003337252
9781000796568
1000796566
Physical Description: 1 online resource (356 pages).

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Table of contents

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020 |a 9781523138951 
020 |a 8770221170 
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035 |a (OCoLC)1280216898 
100 1 |a Chen, Kwang-Cheng,  |e author. 
245 1 0 |a Artificial intelligence in wireless robotics /  |c Kwang-Cheng Chen. 
264 1 |a Gistrup, Denmark :  |b River Publishers,  |c [2020] 
264 4 |c ©2020 
300 |a 1 online resource (356 pages). 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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490 1 |a River Publishers Series in Information Science and Technology 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
505 0 |a Preface xi -- List of Figures xv -- List of Tables xxv -- List of Abbreviations xxvii -- 1 Introduction to Artificial Intelligence and Robotics -- 1 1.1 Common Sense Knowledge of AI, Cybernetics, and Robotics -- 1 1.2 Intelligent Agents 6 -- 1.2.1 The Concept of Rationality 6 -- 1.2.2 System Dynamics 8 -- 1.2.3 Task Environments 9 -- 1.2.4 Robotics and Multi-Agent Systems 12 -- 1.3 Reasoning 14 -- 1.3.1 Constraint Satisfaction Problems 16 -- 1.3.2 Solving CSP by Search 18 References 24 -- 2 Basic Search Algorithms 25 -- 2.1 Problem-Solving Agents 25 -- 2.2 Searching for Solutions 29 -- 2.3 Uniform Search 33 -- 2.3.1 Breadth-First Search 33 -- 2.3.2 Dynamic Programming 36 -- 2.3.3 Depth-first Search 42 -- 2.4 Informed Search 44 -- 2.5 Optimization 49 -- 2.5.1 Linear Programming 49 -- 2.5.2 Nonlinear Programming 50 -- 2.5.3 Convex Optimization 51 References 52 -- 3 Machine Learning Basics 53 -- 3.1 Supervised Learning 55 -- 3.1.1 Regression 55 -- 3.1.2 Bayesian Classification 62 -- 3.1.3 KNN 64 -- 3.1.4 Support Vector Machine 66 -- 3.2 Unsupervised Learning 67 -- 3.2.1 K-Means Clustering 67 -- 3.2.2 EM Algorithms 69 -- 3.2.3 Principal Component Analysis 70 -- 3.3 Deep Neural Networks 73 -- 3.4 Data Preprocessing 76 -- References -- 80 
505 8 |a 4 Markov Decision Processes 81 -- 4.1 Statistical Decisions 81 -- 4.1.1 Mathematical Foundation 85 -- 4.1.2 Bayes Decision 86 -- 4.1.3 Radar Signal Detection 92 -- 4.1.4 Bayesian Sequential Decision 93 -- 4.2 Markov Decision Processes 95 -- 4.2.1 Mathematical Formulation of Markov Decision Process 96 -- 4.2.2 Optimal Policies 99 -- 4.2.3 Developing Solutions to Bellman Equation 100 -- 4.3 Decision Making and Planning: Dynamic Programming 103 -- 4.4 Application of MDP to Search Mobile Target 109 -- 4.5 Multi-Armed Bandit Problem 113 -- 4.5.1 E-Greedy Algorithm 116 -- 4.5.2 Upper Confidence Bounds 116 -- 4.5.3 Thompson Sampling 118 References 126 -- 5 Reinforcement Learning 127 -- 5.1 Fundamentals of Reinforcement Learning 128 -- 5.1.1 Revisit of Multi-Armed Bandit Problem 128 -- 5.1.2 Basics in Reinforcement Learning 132 -- 5.1.3 Reinforcement Learning Based on Markov Decision Process 133 -- 5.1.4 Bellman Optimality Principle 136 -- 5.2 Q-Learning 138 -- 5.2.1 Partially Observable States 138 -- 5.2.2 Q-Learning Algorithm 140 -- 5.2.3 Illustration of Q-Learning 142 -- 5.3 Model-Free Learning 149 -- 5.3.1 Monte Carlo Methods 150 -- 5.3.2 Temporal Difference Learning 153 -- 5.3.3 SARSA 158 -- 5.3.4 Relationship Between Q-Learning and TD-Learning 158 -- References 161 -- 6 State Estimation 163 -- 6.1 Fundamentals of Estimation 163 -- 6.1.1 Linear Estimator from Observations 164 -- 6.1.2 Linear Prediction 167 -- 6.1.3 Bayesian Estimation 168 -- 6.1.4 Maximum Likelihood Estimation 171 -- 6.2 Recursive State Estimation 173 -- 6.3 Bayes Filters 176 -- 6.4 Gaussian Filters 179 -- 6.4.1 Kalman Filter 179 -- 6.4.2 Scalar Kalman Filter 181 -- 6.4.3 Extended Kalman Filter 186 -- References 188 -- 7 Localization 189 -- 7.1 Localization By Sensor Network 190 -- 7.1.1 Time-of-Arrival Techniques 190 -- 7.1.2 Angle-of-Arrival Techniques 193 -- 7.1.3 Time-Difference-of-Arrivals Techniques 196 -- 7.2 Mobile Robot Localization 198 -- 7.3 Simultaneous Localization and Mapping 200 -- 7.3.1 Probabilistic SLAM 200. 
505 8 |a 8 Robot Planning 215 -- 8.1 Knowledge Representation and Classic Logic 215 -- 8.1.1 Bayesian Networks 217 -- 8.1.2 Semantic Representation 224 -- 8.2 Discrete Planning 225 -- 8.3 Planning and Navigation of An Autonomous Mobile Robot 228 -- 8.3.1 Illustrative Example for Planning and Navigation 229 -- 8.3.2 Reinforcement Learning Formulation 230 -- 8.3.3 Fixed Length Planning 233 -- 8.3.4 Conditional Exhaustive Planning 234 -- References 239 -- 9 Multi-Modal Data Fusion 241 -- 9.1 Computer Vision 241 -- 9.1.1 Basics of Computer Vision 243 -- 9.1.2 Edge Detection 244 -- 9.1.3 Image Features and Object Recognition 246 -- 9.2 Multi-Modal Information Fusion Based on Visionary Functionalities 247 -- 9.3 Decision Trees 252 -- 9.3.1 Illustration of Decisions 252 -- 9.3.2 Formal Treatment 255 -- 9.3.3 Classification Trees 256 -- 9.3.4 Regression Trees 257 -- 9.3.5 Rules and Trees 259 -- 9.3.6 Localizing Robot 259 -- 9.3.7 Reinforcement Learning with Decision Trees 262 -- 9.4 Federated Learning 268 -- 9.4.1 Federated Learning Basics 268 -- 9.4.2 Federated Learning Through Wireless Communications 270 -- 9.4.3 Federated Learning over Wireless Networks 271 -- 9.4.4 Federated Learning over Multiple Access Communications 273 -- References 275 -- 10 Multi-Robot Systems 277 -- 10.1 Multi-Robot Task Allocation 278 -- 10.1.1 Optimal Allocation 278 -- 10.1.2 Multiple Traveling Salesmen Problem 281 -- 10.1.3 Factory Automation 282 -- 10.2 Wireless Communications and Networks 287 -- 10.2.1 Digital Communication Systems 288 -- 10.2.2 Computer Networks 292 -- 10.2.3 Multiple Access Communication 294 -- 10.3 Networked Multi-Robot Systems 296 -- 10.3.1 Connected Autonomous Vehicles in Manhattan Streets 296 -- 10.3.2 Networked Collaborative Multi-Robot Systems 306 -- References 313 -- Index 315 -- About the Author 323. 
505 8 |a 7.3.2 SLAM with Extended Kalman Filter 203 -- 7.3.3 SLAM Assisted by Stereo Camera 205 -- 7.4 Network Localization and Navigation 208 -- References 212. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Autonomous robots. 
650 0 |a Wireless communication systems. 
650 0 |a Artificial intelligence. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |z 8770221189 
830 0 |a River Publishers series in information science and technology. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpAIWR0002/artificial-intelligence-in?kpromoter=marc  |y Full text 

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