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.
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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. Its 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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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
Departments:
Aerospace & aviation technology; Robotics;
Record updated at:
19 April, 2013
time:
15:52
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.
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