Search and Classification Using Multiple Autonomous Vehicles [electronic resource] : Decision-Making and Sensor Management / by Yue Wang, Islam I. Hussein.

By: Wang, Yue [author.]Contributor(s): Hussein, Islam I [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Control and Information Sciences: 427Publisher: London : Springer London, 2012Description: XVI, 160p. 51 illus., 46 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781447129578Subject(s): Engineering | Systems theory | Astronautics | Engineering | Control | Systems Theory, Control | Robotics and Automation | Aerospace Technology and AstronauticsAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ212-225Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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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