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Search and Classification Using Multiple Autonomous Vehicles - Decision-Making and Sensor Management (Paperback, 2012 ed.) Loot Price: R1,487
Discovery Miles 14 870
Search and Classification Using Multiple Autonomous Vehicles - Decision-Making and Sensor Management (Paperback, 2012 ed.): Yue...

Search and Classification Using Multiple Autonomous Vehicles - Decision-Making and Sensor Management (Paperback, 2012 ed.)

Yue Wang, Islam I. Hussein

Series: Lecture Notes in Control and Information Sciences, 427

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Loot Price R1,487 Discovery Miles 14 870 | Repayment Terms: R139 pm x 12*

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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.

General

Imprint: Springer London
Country of origin: United Kingdom
Series: Lecture Notes in Control and Information Sciences, 427
Release date: April 2012
First published: 2012
Authors: Yue Wang • Islam I. Hussein
Dimensions: 235 x 155 x 11mm (L x W x T)
Format: Paperback
Pages: 160
Edition: 2012 ed.
ISBN-13: 978-1-4471-2956-1
Categories: Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory
Books > Professional & Technical > Transport technology > Aerospace & aviation technology > General
Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics
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LSN: 1-4471-2956-3
Barcode: 9781447129561

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