Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Matthew J. Robbins, PhD

Abstract

An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as the system evolves. Designed computational experiments of eight problem instances are conducted to compare the two policies with respect to their quality of solution, computational efficiency, and robustness. The ADP policy is superior in 2 of 8 problem instances - those instances with less AUCAV fuel and a low target arrival rate - whereas the DROP policy is superior in 6 of 8 problem instances. The ADP policy outperforms the DROP policy with respect to computational efficiency in all problem instances.

AFIT Designator

AFIT-ENS-MS-21-M-162

DTIC Accession Number

AD1131058

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