Search and Classification Using Multiple Autonomous Vehicles : Decision-Making and Sensor Management

Islam I. Hussein

ISBN 10: 1447129563 / ISBN 13: 9781447129561

Editore: Springer London, Springer, 2012

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9781447129561

Dati bibliografici

• Titolo: Search and Classification Using Multiple Autonomous Vehicles : Decision-Making and Sensor Management
• Editore: Springer London, Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1447129563
• ISBN 13: 9781447129561
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 10 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 10 millimetri di profondità
• Peso: 277 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Yue Wang's research areas include decision-making and sensor management for search, classification and tracking using multiple autonomous vehicles, distributed estimation and control of networked cyber-physical systems, and dynamic coverage control over large-scale domains. She got her Ph.D. degree in Mechanical Engineering at Worcester Polytechnic Institute in May 2011. She is currently a postdoctoral research associate in the Electrical Engineering Department at University of Notre Dame and will be an Assistant Professor in the Mechanical Engineering Department in Clemson University from 2012.
Islam I. Hussein's research areas include nonlinear and evolutionary dynamics, control and optimization, detection and estimation, risk-based decision-making, sensor/communication networks, and agent-based systems. He is an assistant professor in the Mechanical Engineering Department at Worcester Polytechnic Institute. From 2005 to 2006, he held a postdoctoral research associate position at the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He was awarded the Ph.D. degree in Aerospace Engineering in 2005 and the M.Sc. degrees in Aerospace Engineering and Applied Mathematics in 2002 from the University of Michigan.