Information-Driven Planning and Control

by Ferrari, Wettergren

ISBN: 9780262362948 | Copyright 2021

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A unified framework for developing planning and control algorithms for active sensing, with examples of applications for modern sensor technologies.

Active sensor systems, increasingly vital to such applications as unmanned vehicles, mobile robots, and environmental monitoring, are characterized by a high degree of autonomy, reconfigurability, and redundancy. This book is the first to offer a unified framework for the development of planning and control algorithms for active sensing with multiple agents, with application examples including cameras and acoustic and gas sensors. The methods presented are characterized as information-driven because their goal is to optimize the value of information, rather than to optimize traditional guidance and navigation objectives.

The book explains relevant background in systems and control, graph, probability, and information theories; develops an integrated mathematical representation, or model, of system components and their interactions; and shows how motion planning, network, and control theoretic algorithms can be used to manage agent mode, position, and motion. It describes information-driven placement, navigation, and control methods that can be used to allocate limited resources so that sensing objectives, including coverage, detection, classification, and tracking, are optimized. These systems are able to process and learn from data, adapt autonomously to unexpected situations, self-organize to meet multiple objectives, and evolve over time to exhibit greater functionality in changing and complex environments. The book's unified notation and treatment allows direct comparison and parallel implementations of methods and algorithms drawn from disparate communities and disciplines.

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CONTENTS (pg. vii)
LIST OF FIGURES (pg. xi)
LIST OF TABLE (pg. xxiii)
FOREWORD (pg. xxv)
PREFACE (pg. xxvii)
I. MATHEMATICS OF INFORMATION-DRIVEN PLANNING AND CONTROL (pg. 1)
1. DYNAMIC SYSTEMS (pg. 5)
1.1 Feedback Control of Dynamic Systems (pg. 7)
1.2 Modeling of Dynamic Systems (pg. 9)
1.3 Properties of Dynamic Systems (pg. 15)
Exercises (pg. 25)
2. OPTIMAL CONTROL (pg. 29)
2.1 Indirect Solution Approach (pg. 32)
2.2 Direct Solution Approach (pg. 34)
Extensions (pg. 38)
Exercises (pg. 40)
3. GRAPH THEORY (pg. 43)
Extensions (pg. 50)
Exercises (pg. 52)
4. PROBABILITY THEORY (pg. 53)
4.1 Axioms of Probability (pg. 54)
4.2 Conditional Probability and Bayes’ Rule (pg. 56)
4.3 Random Variables (pg. 60)
4.4 Stochastic Processes (pg. 73)
4.5 Markov Decision Processes and Bellman Equation (pg. 79)
Exercises (pg. 85)
5. INFORMATION THEORY (pg. 91)
Exercises (pg. 101)
6. PART I GLOSSARY (pg. 105)
II. SENSOR SYSTEM MODELING (pg. 109)
7. MOBILE PLATFORM MODELS (pg. 113)
7.1 Ground Vehicles (pg. 114)
7.2 Air Vehicles (pg. 119)
7.3 Underwater Vehicles (pg. 135)
Exercises (pg. 143)
8. TARGET MODELS (pg. 147)
8.1 Fixed Targets (pg. 149)
8.2 Moving Targets (pg. 158)
Exercises (pg. 165)
9. SENSOR MODELS (pg. 169)
9.1 Measurement Models (pg. 172)
9.2 Field-of-View (FOV) Model (pg. 202)
Exercises (pg. 213)
10. MODELS OF ENVIRONMENTAL VARIABILITY (pg. 219)
10.1 Gaussian Mixture Environmental Modeling (pg. 221)
10.2 Kriging Environmental Interpolation (pg. 225)
Exercises (pg. 229)
11. PART II GLOSSARY (pg. 231)
III. SENSING PERFORMANCE AND OBJECTIVE FUNCTIONS (pg. 235)
12. COVERAGE (pg. 241)
12.1 Area Coverage (pg. 242)
12.2 Track Coverage (pg. 254)
Exercises (pg. 263)
13. DETECTION (pg. 265)
13.1 Target Search (pg. 266)
13.2 Target Detection (pg. 273)
13.3 Track Detection (pg. 278)
Exercises (pg. 290)
14. CLASSIFICATION (pg. 293)
Exercises (pg. 303)
15. TRACKING AND LOCALIZATION (pg. 305)
15.1 Kinematic-Matching Approach (pg. 308)
15.2 Likelihood Function Approach (pg. 316)
15.3 Bayesian Approach (pg. 325)
15.4 Target Localization (pg. 336)
Exercises (pg. 344)
16. PART III GLOSSARY (pg. 347)
IV. INFORMATION-DRIVEN PLACEMENT AND OPTIMIZATION (pg. 351)
17. PACKING ALGORITHMS (pg. 357)
17.1 Circle Packing for Omnidirectional Sensor Networks (pg. 359)
Exercises (pg. 368)
18. VORONOI DIAGRAMS (pg. 371)
18.1 Locational Optimization (pg. 374)
18.2 Geometrical Optimization (pg. 382)
Exercises (pg. 387)
19. MULTI-OBJECTIVE OPTIMIZATION (pg. 391)
19.1 Unconstrained Gradient Optimization of a Global Criterion (pg. 396)
19.2 Constrained Optimization (pg. 404)
Exercises (pg. 428)
20. METAHEURISTIC OPTIMIZATION (pg. 433)
20.1 Grid Coverage and Optimization (pg. 435)
20.2 Simulated Annealing (pg. 446)
20.3 Genetic Algorithms (pg. 456)
Exercises (pg. 472)
21. OPTIMAL PLACEMENT OF DYNAMIC SENSORS (pg. 477)
Exercises (pg. 488)
22. PART IV GLOSSARY (pg. 489)
V. INFORMATION-DRIVEN PLANNING AND CONTROL METHODS (pg. 493)
23. SENSOR TRAJECTORY OPTIMIZATION (pg. 499)
Exercises (pg. 511)
24. SENSOR PATH PLANNING (pg. 515)
24.1 Cell Decomposition (pg. 525)
24.2 Probabilistic Roadmap (pg. 534)
24.3 Navigation Function (pg. 544)
Exercises (pg. 565)
25. INTEGRATED SENSOR PLANNING AND CONTROL (pg. 571)
Exercises (pg. 582)
26. PART V GLOSSARY (pg. 587)
REFERENCES (pg. 591)
CONTRIBUTORS (pg. 623)
INDEX (pg. 625)

Silvia Ferrari

Silvia Ferrari is John Brancaccio Professor of Mechanical and Aerospace Engineering in the Sibley School of Mechanical and Aerospace Engineering at Cornell University.

Thomas A. Wettergren

Thomas A. Wettergren is Research Scientist in Applied Mathematics and Adjunct Professor at the University of Rhode Island.

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