© 2012

Search and Classification Using Multiple Autonomous Vehicles

Decision-Making and Sensor Management


  • Shows the reader how to undertake effective resource allocation in multiple-vehicle systems with sensing limitations and in large-scale mission domains

  • Detailed analysis, simulation and results demonstrate algorithm implementation in a manner that readers can easily replicate and develop further

  • Treats both deterministic and probabilistic approaches to domain search and object classification in a comprehensive and well-referenced style so that readers can select the more appropriate approach


Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 427)

Table of contents

  1. Front Matter
    Pages 1-12
  2. Yue Wang, Islam I. Hussein
    Pages 1-9
  3. Yue Wang, Islam I. Hussein
    Pages 11-67
  4. Yue Wang, Islam I. Hussein
    Pages 69-78
  5. Yue Wang, Islam I. Hussein
    Pages 79-88
  6. Yue Wang, Islam I. Hussein
    Pages 89-121
  7. Yue Wang, Islam I. Hussein
    Pages 145-148
  8. Back Matter
    Pages 0--1

About this book


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.


Bayesian-Based Risk Analysis Coverage Control Detection and Estimation Lyapunov Stability Analysis Multi-vehicle Cooperation Path Planning

Authors and affiliations

  1. 1., Department of Electrical EngineeringUniversity of Notre DameNotre DameUSA
  2. 2., Department of Mechanical EngineeringWorcester Polytechnic InstituteWorcesterUSA

About the authors

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.

Bibliographic information

Industry Sectors
Chemical Manufacturing
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Materials & Steel
Oil, Gas & Geosciences


From the reviews:

“In the present book, real-time, decision-making strategies are investigated for domain search and object classification using multiple autonomous vehicle systems under limited sensory resources. … The book may serve as a tool for students, scientists and engineers from academia and industry experienced in these attractive areas. It can be also a source for courses, which can be part of the academic program of Electrical, Control and Computer Science Departments.” (Clementina Mladenova, Zentralblatt MATH, Vol. 1244, 2012)

“Two notable features of the book are that it is rather terse and that it provides a diversity of cases whose differences are left for the reader to monitor. … As an overview of the subject, the book is certainly valuable.” (A. F. Gualtierotti, Mathematical Reviews, January, 2013)