Algorithms for persistent autonomy and surveillance
Name
1005227829-MIT.pdf
Description
Full printable version
Size
9.51 MB
Format
Adobe PDF
Checksum (MD5)
94a851057afe3dd6e1e4f0551625c5f6
Author(s)
Baykal, Cenk
Advisor(s)
Daniela Rus.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we consider the problem of monitoring stochastic, time-varying events occurring at discrete locations. Our problem formulation extends prior work in persistent surveillance by considering the objective of successfully completing monitoring tasks in unknown, dynamic environments where the rates of events are time-inhomogeneous and may be subject to abrupt changes. We propose novel monitoring algorithms that effectively strike a balance between exploration and exploitation as well as a balance between remembering and discarding information to handle temporal variations in unknown environments. We present analysis proving the favorable properties of the policies generated by our algorithms and present simulation results demonstrating their effectiveness in several monitoring scenarios inspired by real-world applications. Our theoretical and empirical results support the applicability of our algorithm to a wide range of monitoring applications, such as detection and tracking efforts at a large scale.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 67-70).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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