Hirsch / Pardalos / Murphey Dynamics of Information Systems

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

E-Book, Englisch, Band 40, 372 Seiten, eBook

Reihe: Springer Optimization and Its Applications

Our understanding of information and information dynamics has outgrown classical information theory. The theory does not account for the value or influence of information within the context of a system or network and does not explain how these properties might influence how information flows though and interacts with a system. The invited chapters in this collection present new theories, methods, and applications that address some of these limitations.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.

These newly developed techniques have numerous applications including: the detection of terrorist networks, the design of highly functioning businesses and computer systems, modeling the distributed sensory and control physiology of animals, quantum entanglement and genome modeling, multi-robotic systems design, as well as industrial and manufacturing safety.

Zielgruppe

Research

Autoren/Hrsg.

Hirsch, Michael

Pardalos, Panos M.

Murphey, Robert

Inhaltsverzeichnis

The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams.- Information Trajectory of Optimal Learning.- Performance-Information Analysis and Distributed Feedback Stabilization in Large-Scale Interconnected Systems.- A General Approach for Modules Identification in Evolving Networks.- Topology Information Control in Feedback Based Reconfiguration Processes.- Effect of Network Geometry and Interference on Consensus in Wireless Networks.- Analyzing the Theoretical Performance of Information Sharing.- Self-Organized Criticality of Belief Propagation in Large Heterogeneous Teams.- Effect of Humans on Belief Propagation in Large Heterogeneous Teams.- Integration of Signals in Complex Biophysical Systems.- An Info-Centric Trajectory Planner for Unmanned Ground Vehicles.- Orbital Evasive Target Tracking and Sensor Management.- Decentralized Cooperative Control of Autonomous Surface Vehicles.- A Connectivity Reduction Strategy for Multi-agent Systems.- The Navigation Potential of Ground Feature Tracking.- Minimal Switching Time of Agent Formations with Collision Avoidance.- A Moving Horizon Estimator Performance Bound.- A p-norm Discrimination Model for Two Linearly Inseparable Sets.- Local Neighborhoods for the Multidimensional Assignment Problem.

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)."