Faculty Advisor

Stephen S. Nestinger

Faculty Advisor

Richard S. Erwin

Faculty Advisor

David J. Olinger

Faculty Advisor

Michael A. Demetriou

Faculty Advisor

Islam I. Hussein

Identifier

etd-040111-000933

Abstract

This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms.

Publisher

Worcester Polytechnic Institute

Degree Name

PhD

Department

Mechanical Engineering

Project Type

Dissertation

Date Accepted

2011-04-01

Accessibility

Unrestricted

Subjects

Domain search, Decision-making, Object classification, Multiple autonomous vehicles, Dynamic coverage control, Sensor management, Bayesian sequential detection and estimation

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