Hirsch / Pardalos / Murphey Dynamics of Information Systems

Theory and Applications
1. Auflage 2010
ISBN: 978-1-4419-5689-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 40, 372 Seiten

Reihe: Springer Optimization and Its Applications

'Dynamics of Information Systems' presents state-of-the-art research explaining the importance of information in the evolution of a distributed or networked system. This book presents techniques for measuring the value or significance of information within the context of a system. Each chapter reveals a unique topic or perspective from experts in this exciting area of research. This volume is intended for graduate students and researchers interested in the most recent developments in information theory and dynamical systems, as well as scientists in other fields interested in the application of these principles to their own area of study.

Autoren/Hrsg.

Hirsch, Michael

Pardalos, Panos M.

Murphey, Robert

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams;15
3.1;1.1 Introduction;15
3.2;1.2 Key Subproblems Arising in the Context of Dynamic Information Extraction;16
3.3;1.3 Nonlinear Embedding of Dynamic Data;19
3.4;1.4 Structure Extraction from High Dimensional Data Streams;21
3.5;1.5 Robust Dynamic Data Segmentation;24
3.6;1.6 Constrained Interpolation of High Dimensional Signals;31
3.7;1.7 Hypothesis Testing and Data Sharing;34
3.8;1.8 Conclusions;39
3.9;References;39
4;Information Trajectory of Optimal Learning;42
4.1;2.1 Introduction;42
4.2;2.2 Topology and Geometry of Learning Systems;45
4.3;2.3 Optimal Evolution and Bounds;50
4.4;2.4 Empirical Evaluation on Learning Agents;53
4.5;2.5 Conclusion;56
4.6;References;57
5;Performance-Information Analysis and Distributed Feedback Stabilization in Large- Scale Interconnected Systems;58
5.1;3.1 Introduction;58
5.2;3.2 Problem Formulation;61
5.3;3.3 Performance-Information Analysis;65
5.4;3.4 Problem Statements;75
5.5;3.5 Distributed Risk-Averse Feedback Stabilization;87
5.6;3.6 Conclusions;93
5.7;References;94
6;A General Approach for Modules Identification in Evolving Networks;95
6.1;4.1 Introduction;96
6.2;4.2 Preliminaries and Problem Definition ;97
6.3;4.3 Compact Representation of a Network;98
6.4;4.4 Partition Based on Evolution History;104
6.5;4.5 Experimental Evaluation;108
6.6;4.6 Conclusions;111
6.7;References;111
7;Topology Information Control in Feedback Based Reconfiguration Processes;113
7.1;5.1 Introduction and Motivation;113
7.2;5.2 Group Communication Networking;115
7.3;5.3 Reconfiguration Process Optimization;120
7.4;5.4 Topology Information Control ;125
7.5;5.5 Concluding Remarks;134
7.6;References;135
8;Effect of Network Geometry and Interference on Consensus in Wireless Networks;137
8.1;6.1 Introduction;137
8.2;6.2 Problem Formulation;138
8.3;6.3 Analysis of a Ring and a 2D Torus ;141
8.4;6.4 Hierarchical Networks;150
8.5;6.5 Conclusions;153
8.6;References;154
9;Analyzing the Theoretical Performance of Information Sharing;156
9.1;7.1 Introduction;156
9.2;7.2 Information Sharing;158
9.3;7.3 Experimental Results;160
9.4;7.4 RelatedWork;172
9.5;7.5 Conclusions and Future Work;173
9.6;References;174
10;Self-Organized Criticality of Belief Propagation in Large Heterogeneous Teams;176
10.1;8.1 Introduction;176
10.2;8.2 Self-Organized Criticality;178
10.3;8.3 Belief Sharing Model;179
10.4;8.4 System Operation Regimes;180
10.5;8.5 Simulation Results;181
10.6;8.6 RelatedWork;192
10.7;8.7 Conclusions and Future Work;193
10.8;References;193
11;Effect of Humans on Belief Propagation in Large Heterogeneous Teams;194
11.1;9.1 Introduction;194
11.2;9.2 Self-Organized Critical Systems;196
11.3;9.3 The Enabler-Impeder Effect;196
11.4;9.4 Model of Information Dissemination in a Network;197
11.5;9.5 Simulation Results;198
11.6;9.6 RelatedWork;205
11.7;9.7 Conclusions and Future Work;206
11.8;References;206
12;Integration of Signals in Complex Biophysical Systems;208
12.1;10.1 Introduction;209
12.2;10.2 Methods for Analysis of Phase Synchronization;210
12.3;10.3 Analysis of the Data Collected During Sensory-Motor Experiments;214
12.4;10.4 Conclusion;220
12.5;References;221
13;An Info-Centric Trajectory Planner for Unmanned Ground Vehicles;223
13.1;11.1 Introduction;223
13.2;11.2 Problem Formulation and Background;225
13.3;11.3 Obstacle Motion Studies ;227
13.4;11.4 Target Motion Studies ;238
13.5;11.5 Conclusion;241
13.6;References;241
14;Orbital Evasive Target Tracking and Sensor Management;243
14.1;12.1 Introduction;243
14.2;12.2 Fundamentals of Space Target Orbits ;245
14.3;12.3 Modeling Maneuvering Target Motion in Space Target Tracking ;247
14.4;12.4 Sensor Management for Situation Awareness ;251
14.5;12.5 Simulation Study ;254
14.6;12.6 Summary and Conclusions;257
14.7;References;264
15;Decentralized Cooperative Control of Autonomous Surface Vehicles;266
15.1;13.1 Introduction;266
15.2;13.2 Motivation;267
15.3;13.3 Decentralized Hierarchical Supervisor;267
15.4;13.4 Simulation Results;279
15.5;13.5 Conclusion and Future Work;281
15.6;References;282
16;A Connectivity Reduction Strategy for Multi- agent Systems;283
16.1;14.1 Introduction;283
16.2;14.2 Background ;284
16.3;14.3 A Distributed Scheme of Graph Reduction;286
16.4;14.4 Discussion and Simulation;292
16.5;14.5 Conclusion;294
16.6;References;294
17;The Navigation Potential of Ground Feature Tracking;295
17.1;15.1 Introduction;295
17.2;15.2 Modeling;297
17.3;15.3 Special Cases;300
17.4;15.4 Nondimensional Variables;303
17.5;15.5 Observability;305
17.6;15.6 Only the Elevation zp of the Tracked Ground Object is Known;308
17.7;15.7 Partial Observability;310
17.8;15.8 Conclusion;310
17.9;References;311
18;Minimal Switching Time of Agent Formations with Collision Avoidance;312
18.1;16.1 Introduction;312
18.2;16.2 Problem Definition;315
18.3;16.3 Dynamic Programming Formulation;317
18.4;16.4 Computational Implementation;320
18.5;16.5 Computational Experiments;324
18.6;16.6 Conclusion;326
18.7;References;327
19;A Moving Horizon Estimator Performance Bound;329
19.1;17.1 Introduction;329
19.2;17.2 Linear State Estimation;330
19.3;17.3 MHE Performance Bound;333
19.4;17.4 Simulation and Analysis;336
19.5;17.5 Future Work;340
19.6;References;340
20;A p- norm Discrimination Model for Two Linearly Inseparable Sets;341
20.1;18.1 Introduction;341
20.2;18.2 The p- norm Linear Separation Model;343
20.3;18.3 Implementation of p- order Conic Programming Problems via Polyhedral Approximations;347
20.4;18.4 Case Study;355
20.5;18.5 Conclusions;357
20.6;References;357
21;Local Neighborhoods for the Multidimensional Assignment Problem;359
21.1;19.1 Introduction;359
21.2;19.2 Neighborhoods;361
21.3;19.3 Extensions;370
21.4;19.4 Discussion;375
21.5;References;376

Leseproben

"Chapter 11 An Info-Centric Trajectory Planner for Unmanned Ground Vehicles (p. 213-214)

Michael A. Hurni, Pooya Sekhavat, and I. Michael Ross

Summary We present a pseudospectral (PS) optimal control framework for autonomous trajectory planning and control of an Unmanned Ground Vehicle (UGV) with real-time information updates. The algorithm is introduced and implemented on a collection of motion planning scenarios with varying levels of information. The UGV mission is to traverse from an initial start point and reach the target point in minimum time, with maximum robustness, while avoiding both static and dynamic obstacles.

This is achieved by computing the control solution that solves the initial planning problem by minimizing a cost function while satisfying dynamical and environmental constraints based on the initial global knowledge of the area. To overcome the problem of incomplete global knowledge and a dynamic environment, the UGV uses its sensors to map the locally detected changes in the environment and continuously updates its global map. At each information update, the optimal control is recomputed and implemented. Simulation results illustrate the performance of the planner under varying levels of information.

11.1 Introduction

Autonomous trajectory planning of unmanned vehicles has been one of the main goals in robotics for several years. In recent years, this problem has become particularly important as a result of rapid growth in its applications to both military and civilian missions. Various control methods have been proposed and examined for autonomous guidance and control of unmanned vehicles [4, 13].

There are two approaches to optimal trajectory planning for a dynamic system: The decoupled approach and the direct approach. The decoupled approach involves first searching for a path (using a path planner) and then finding a time-optimal control solution on the path subject to the actuator limits. The direct approach searches for the optimal control trajectory directly within the system’s state space [4]. Optimal control trajectory planning using numerical optimization, as described in [4], is a direct approach to the complete motion-planning problem, which determines the path to the target by searching for the optimal control trajectory within the vehicle’s state space. The result is the complete state space and control solution from start to goal.

The basic concept of how optimal path planning works follows from [4, 9]. The planner is given the kinodynamic equations of the vehicle, the obstacles’ approximate location and geometry (to be coded into smooth path constraint functions), and the mission’s boundary conditions and cost function. The kinodynamic equations can also be viewed as constraints (similar to the obstacles), defining the relationship between the vehicle state and the control input.

The actual obstacles’ geometry need not be smooth, but the constraints used to mathematically define the obstacles must be made up of one or more smooth functions. The optimal control technique finds a solution to the state equations that takes the vehicle from the initial state at time zero to the final state at the final time, while avoiding obstacles, obeying vehicle state and control limits, and minimizing the cost function. The cost function can be any function of state variables, control variables and time, as long as it is sufficiently smooth (i.e., continuous and differentiable)."