Search and Classification Using Multiple Autonomous Vehicles

by: Yue Wang - Islam I. Hussein

Search and Classification Using Multiple Autonomous Vehicles
Author: Yue Wang, Islam I. Hussein

Publisher: Springer London Ltd

Series: Lecture Notes in Control and Information Sciences

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Format: Paperback / softback

Publication date: 31 March 2012

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ISBN: 1447129563 ISBN 13: 9781447129561

Search and Classification Using Multiple Autonomous Vehicles by Yue Wang - Islam I. Hussein

This book is a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAVs) under both deterministic and probabilistic frameworks. It provides detailed analysis, simulation and results. Top page

Complete description

Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. Academic researchers and graduate students from aerospace, robotics, mechanical or electrical engineering backgrounds interested in multi-agent coordination and control, in detection and estimation or in Bayes filtration will find this text of interest. Top page

General info

Publisher & Imprint: Springer London Ltd

City: England

Pages: 176

More info: height 235 mm width 155 mm weight 278 gr thickness 13 mm

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Age recommended: Professional and scholarly

Subject Indexing & Classification Dewey:(DC23) 629.4 Library of Congress Subject: TA1-2040 Engineering

Departments: Aerospace & aviation technology; Robotics;

Record updated at: 19 August, 2014 time: 19:41

Summary Search and Classification Using Multiple Autonomous Vehicles Coverage Control.- Awareness-based Decision-making Strategy.- Bayesian-based Decision-making Strategy.- Risk-based Sequential Decision-making Strategy.- Risk-based Sensor Management for Integrated Detection and Estimation.- Conclusion and Future Work. Top page

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