Algorithms for persistent autonomy and surveillance

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Baykal, Cenk

Advisor dc:contributor.advisor

• Daniela Rus.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111862

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111862