Multi-objective optimization of next-generation aircraft collision avoidance software
Name
920857584-MIT.pdf
Description
Full printable version
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1006.04 KB
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925dc3a833626acef3bb770d3d0a458d
Author(s)
Lepird, John R
Advisor(s)
Michael P. Owen and Dimitri P. Bertsekas.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Developed in the 1970's and 1980's, the Traffic Alert and Collision Avoidance System (TCAS) is the last safety net to prevent an aircraft mid-air collision. Although TCAS has been historically very effective, TCAS logic must adapt to meet the new challenges of our increasingly busy modern airspace. Numerous studies have shown that formulating collision avoidance as a partially-observable Markov decision process (POMDP) can dramatically increase system performance. However, the POMDP formulation relies on a number of design parameters modifying these parameters can dramatically alter system behavior. Prior work tunes these design parameters with respect to a single performance metric. This thesis extends existing work to handle more than one performance metric. We introduce an algorithm for preference elicitation that allows the designer to meaningfully define a utility function. We also discuss and implement a genetic algorithm that can perform multi-objective optimization directly. By appropriately applying these two methods, we show that we are able to tune the POMDP design parameters more effectively than existing work.
Description
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"June 2015." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-90).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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