Titlebook: Search and Classification Using Multiple Autonomous Vehicles; Decision-Making and Yue Wang,Islam I. Hussein Book 2012 Springer-Verlag Lond

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Introduction,lated literature on MAV systems, domain search, object classification and tracking, and decision-making strategies. Section 1.3 summarizes the organization of this book. Section 1.4 lists the research contribution.

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Conclusion and Future Work,l the unknown objects in the domain. We further extend the risk-based detection method to Bayesian sequential estimation and present an integrated detection and estimation scheme for sensor management. A list of future research directions relating to this book is also provided.

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0170-8643 ion domains.Detailed analysis, simulation and results demonsSearch 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 prob

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Introduction,d sensory resources using Multiple Autonomous Vehicles (MAV) over large-scale domains. In this chapter, we provide an overview of the literature on MAVs and their applications in search and classifications. Section 1.1 introduces the motivation and objectives of this book. Section 1.2 reviews the re

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Coverage Control,erage of a mission domain is equivalent to the detection of all the unknown objects within that domain. A literature review on cooperative coverage control is provided in Section 2.1. Section 2.2 studies deterministic Lyapunov-based coverage control. The limited-range sensor model and vehicle motion

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Awareness-Based Decision-Making Strategy,oundation for the decision-making strategies developed in the subsequent chapters. This chapter presents deterministic awareness-based decision-making strategies for the search and classification of multiple unknown objects of interest using MAVs. This is based on the awareness model developed in Se

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Bayesian-Based Decision-Making Strategy,sing under both centralized and decentralized system architectures. This chapter focuses on the development of real-time decision-making criteria given limited sensory resources under probabilistic frameworks. The uncertainties in sensor perception is taken into account during MAV decision-making. B

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