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Search and Classification Using Multiple Autonomous Vehicles - Decision-Making and Sensor Management (Paperback, 2012 ed.)
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Search and Classification Using Multiple Autonomous Vehicles - Decision-Making and Sensor Management (Paperback, 2012 ed.)
Series: Lecture Notes in Control and Information Sciences, 427
Expected to ship within 10 - 15 working days
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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.
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