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E-raamat: Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields

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  • Formaat: PDF+DRM
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 23-Sep-2011
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849192583
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Adaptive Sampling with Mobile WSN: Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fieldsSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 23-Sep-2011
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781849192583
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Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields. With a single mobile sensor, several approaches are presented to solve the problem of where to sample next to maximally and simultaneously reduce uncertainty in the field estimate and uncertainty in the localisation of the mobile sensor while respecting the dynamics of the time-varying field and the mobile sensor. A case study of mapping a forest fire is presented. Multiple static and mobile sensors are considered next, and distributed algorithms for adaptive sampling are developed resulting in the Distributed Federated Kalman Filter. However, with multiple resources a possibility of deadlock arises and a matrix-based discrete-event controller is used to implement a deadlock avoidance policy. Deadlock prevention in the presence of shared and routing resources is also considered. Finally, a simultaneous and adaptive localisation strategy is developed to simultaneously localise static and mobile sensors in the WSN in an adaptive manner. Experimental validation of several of these algorithms is discussed throughout the book.
Preface viii
PART I Preliminaries
1(20)
1 Introduction
3(8)
1.1 Adaptive sampling for density estimation using WSN
4(2)
1.2 Simultaneous adaptive localization
6(1)
1.3 Discrete event controller for resource scheduling
7(2)
1.4 Summary
9(2)
2 Test beds for theory
11(10)
2.1 Adaptive sampling test bed
11(7)
2.1.1 Objectives of the test bed
11(1)
2.1.2 Structure of the test bed
12(3)
2.1.3 Calibration algorithms
15(3)
2.2 Mobile WSN test bed
18(3)
2.2.1 Objectives of the test bed
18(1)
2.2.2 Structure of the test bed
18(3)
PART II Single-robot adaptive sampling
21(70)
3 Adaptive sampling of parametric fields
23(40)
3.1 Sampling
23(1)
3.2 Sampling for density estimation
24(4)
3.2.1 Clustering
25(1)
3.2.2 Parametric approximation
26(2)
3.2.3 Parameter estimation
28(1)
3.3 Sampling using static wireless sensor network
28(1)
3.4 Sampling using robotic sensor deployment
29(2)
3.4.1 Parametric field representation
29(1)
3.4.2 Non-parametric field representation
30(1)
3.5 Adaptive sampling problem for robots
31(1)
3.6 Sampling strategies: where to sample
32(1)
3.6.1 Global search AS
32(1)
3.6.2 Heuristic greedy AS
33(1)
3.7 Basic EKF formulation
33(28)
3.7.1 Least squares estimation for linear-in-parameters field
35(2)
3.7.2 Kalman filter estimation for linear-in-parameters field with no uncertainty in localization
37(9)
3.7.3 Kalman filter estimation for single Gaussian field with no uncertainty in localization
46(2)
3.7.4 Simple Kalman filter estimation for linear field with uncertainty in localization
48(6)
3.7.5 Kalman filter estimation for linear-in-parameters field with location measurement unavailable
54(7)
3.8 Summary
61(2)
4 Case study: application to forest fire mapping
63(28)
4.1 Parametric description of forest fire spread
63(6)
4.1.1 Simple elliptical fire spread model
65(2)
4.1.2 Complex cellular automata-based discrete event model
67(2)
4.2 Neural network for parameterization
69(2)
4.3 EKF adaptive sampling of spatio-temporal distributions using mobile agents
71(5)
4.3.1 Formulation for elliptically constrained single Gaussian time-varying field
72(1)
4.3.2 Formulation of the general multi-scale algorithm EKF-NN-GAS for fire fields
73(3)
4.4 Potential field to aid navigation through fire field using mobile agents
76(2)
4.5 Simulation results
78(12)
4.5.1 Elliptically constrained single Gaussian time-varying forest fire field
78(2)
4.5.2 RBF-NN parameterization using low-resolution information
80(3)
4.5.3 Sum-of-Gaussians stationary field
83(1)
4.5.4 Sum-of-Gaussians time-varying field
83(3)
4.5.5 Complex RBF time-varying field
86(3)
4.5.6 Potential fields for safe trajectory generation
89(1)
4.6 Summary
90(1)
PART III Multi-resource strategies
91(78)
5 Distributed processing for multi-robot sampling
93(20)
5.1 Completely centralized filter
94(1)
5.2 Completely decentralized filter
95(2)
5.3 Partially centralized federated filter
97(1)
5.4 Distributed federated Kalman filter
98(9)
5.4.1 Partitioning of sampling area
98(2)
5.4.2 Distributed computations and communications
100(7)
5.5 Simulation results
107(2)
5.5.1 Sampling of complex field with centralized AS algorithm using four robots along with partitioning of sampling area
107(2)
5.6 Experimental results
109(2)
5.6.1 Sampling of linear colour field with centralized AS algorithm using two robots
109(1)
5.6.2 Sampling of complex fire field with centralized AS algorithm using two robots
109(2)
5.7 Summary
111(2)
6 Resource scheduling
113(22)
6.1 Matrix-based discrete event controller
113(2)
6.2 Deadlock avoidance
115(2)
6.2.1 Deadlock avoidance policy
116(1)
6.3 Routing resources
117(4)
6.3.1 DEC representation for routing
118(1)
6.3.2 Deadlock avoidance policy for flexible routing systems
119(2)
6.4 Simulation and experimental results
121(12)
6.4.1 Simulation and experimental results for deadlock avoidance
123(7)
6.4.2 Simulation results for routing
130(3)
6.5 Summary
133(2)
7 Adaptive localization
135(34)
7.1 Sensor localization using mobile robot
135(5)
7.1.1 Scenario
135(1)
7.1.2 Robot control
136(1)
7.1.3 Sensor node Kalman filter
137(2)
7.1.4 Simulation results
139(1)
7.2 Simultaneous mobile robot and sensor localization
140(7)
7.2.1 Mobile robot localization
141(5)
7.2.2 Simulation results
146(1)
7.3 Simultaneous adaptive localization
147(14)
7.4 Extensions
161(7)
7.4.1 Effect of uncertainty matrices
161(1)
7.4.2 Effect of radio range and irregularity
161(3)
7.4.3 Energy considerations
164(1)
7.4.4 Extensions to simultaneous adaptive localization
165(3)
7.5 Summary
168(1)
References 169(10)
Index 179
Koushil Sreenath is a Ph.D. candidate in Electrical Engineering at the University of Michigan, Ann Arbor.



Muhammad F. Mysorewala is an Assistant Professor of Systems Engineering at King Fahd University of Petroleum and Minerals, Saudi Arabia.



Dan O. Popa is an Associate Professor of Electrical Engineering at the University of Texas, Arlington.



Frank L. Lewis is a Professor of Electrical Engineering and Moncrief-O'Donnell Chair at the University of Texas, Arlington.
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