Date of Award

Fall 11-22-2024

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering

First Advisor

Matthew J. Rutherford

Second Advisor

Ryan Elmore

Third Advisor

Kimon Valavanis

Fourth Advisor

Haluk Ogmen

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Deep learning, Mechatronics, Metal detector, Pulsed induction, Robotics, Signal processing

Abstract

A metal detector that minimizes human exposure to a hazard area is described. The metal detector is designed for integration with a robot platform, such that the operator’s time in the hazard area can be reduced. Existing metal detectors require human oversight within the hazard area for all operations. Phase 1 of the present research comprises an initial proof of concept. The detector is a static apparatus, and the target is manipulated by the operator. A neural network identifies and classifies targets. In phase 2 the detector is a physical arm which manipulates the detector over a static target. The neural network is capable of determining whether the detector passed over a target in a preceding time-frame. Phase 3 includes more specific target identification including identifying the position of targets and improves the arm construction. The novel research enables a robot system that may operate a safe standoff distance from the operator, capable of detecting, classifying, and locating targets.

Copyright Date

11-2024

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Matthew L. Miller

Provenance

Received from Author

File Format

application/pdf

Language

English (eng)

Extent

148 pgs

File Size

53.4 MB

Download

Available for download on Thursday, January 21, 2027



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