SELF-ASSESSMENT FOR ACTIVE TERRAIN-AIDED NAVIGATION
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Authors
Agustin, Amanda Marie
Subjects
active terrain-aided navigation
ATAN
self-assessment
Partially Observable Monte Carlo Planning
POMCP
Kullback-Leibler Divergence
KLD
autonomous systems
robotics
information theoretic framework
unmanned vehicles
autonomous underwater vehicle
AUV
ATAN
self-assessment
Partially Observable Monte Carlo Planning
POMCP
Kullback-Leibler Divergence
KLD
autonomous systems
robotics
information theoretic framework
unmanned vehicles
autonomous underwater vehicle
AUV
Advisors
Horner, Douglas P.
Date of Issue
2024-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Unmanned systems are reaching a level where the United States Navy (USN) is changing the way it fights to incorporate Manned-Unmanned Teaming. For these teams to be effective, there needs to be a means of communicating information that all members of the team can understand. A unified quantification of expected performance can be addressed with a three-phase information theoretic self-assessment framework: pre-mission, during mission, and post-mission. This framework can be applied to any model of autonomy that contains a plant, a sensor model, a position estimator, a map estimator, and a path planner. This thesis focused on the pre-mission phase implemented on an autonomous underwater vehicle (AUV), using active terrain-aided navigation (ATAN). The dynamic path planning algorithm that ATAN uses, also known as the Partially Observable Monte Carlo Planning (POMCP) process, was revised to utilize a shared information theoretic metric, Kullback Leibler Divergence (KLD), to quantify reward. The pre-mission analysis aimed to understand the relationship between algorithm parameters and information in the environment. The results of this thesis validate a methodology for information theoretic self-assessment. Self-assessment has the potential to enhance the reliability and effectiveness of a model of autonomy by enabling it to evaluate and to clearly communicate its expected performance before, during, and after a mission.
Type
Thesis
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Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.