UXO detection, characterization and remediation using intelligent robotic systems

Saed Amer; Amir Shirkhodaie; Haroun Rababaah

doi:10.1117/12.777778

UXO detection, characterization and remediation using intelligent robotic systems

Saed Amer
, Amir Shirkhodaie
, Haroun Rababaah

Management Science & Engineering

Tennessee State Univ.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

An intelligent robotic system can be distinguished from other machines by its ability to sense, learn, and react to its environment despite various task uncertainties. One of the most powerful sensing modality for robotic system is vision as it enables the robot to see its environment, recognize objects around it and interact with objects to accomplish its task. This paper discusses vision enabling techniques that allows a robot to detect, characterize, classify, and discriminate UneXploded Ordnance (UXO) from clutters in unstructured environments. A soft-computing approach is proposed and validated via indoor and outdoor experiments to measure its performance efficiency and effectiveness in correctly detection and classifying UXO vs. XO and other clutter. The proposed technique has many potential applications for military, homeland security, law enforcement, and in particular, environment UXO remediation and clean-up operations.

Original language	British English
Title of host publication	Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
DOIs	https://doi.org/10.1117/12.777778
State	Published - 2008
Event	Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII - Orlando, FL, United States Duration: 17 Mar 2008 → 20 Mar 2008

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6953
ISSN (Print)	0277-786X

Conference

Conference	Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Country/Territory	United States
City	Orlando, FL
Period	17/03/08 → 20/03/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 10 Reduced Inequalities

Keywords

Color-based classifiers
K-means clustering
Neural networks
Shape-based classifiers
Template matching
Texture-based classifiers
Unexploded ordnance detection and remediation

Access to Document

10.1117/12.777778

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title = "UXO detection, characterization and remediation using intelligent robotic systems",

abstract = "An intelligent robotic system can be distinguished from other machines by its ability to sense, learn, and react to its environment despite various task uncertainties. One of the most powerful sensing modality for robotic system is vision as it enables the robot to see its environment, recognize objects around it and interact with objects to accomplish its task. This paper discusses vision enabling techniques that allows a robot to detect, characterize, classify, and discriminate UneXploded Ordnance (UXO) from clutters in unstructured environments. A soft-computing approach is proposed and validated via indoor and outdoor experiments to measure its performance efficiency and effectiveness in correctly detection and classifying UXO vs. XO and other clutter. The proposed technique has many potential applications for military, homeland security, law enforcement, and in particular, environment UXO remediation and clean-up operations.",

keywords = "Color-based classifiers, K-means clustering, Neural networks, Shape-based classifiers, Template matching, Texture-based classifiers, Unexploded ordnance detection and remediation",

author = "Saed Amer and Amir Shirkhodaie and Haroun Rababaah",

year = "2008",

doi = "10.1117/12.777778",

language = "British English",

isbn = "9780819471444",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII",

note = "Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII ; Conference date: 17-03-2008 Through 20-03-2008",

}

Amer, S, Shirkhodaie, A & Rababaah, H 2008, UXO detection, characterization and remediation using intelligent robotic systems. in Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII., 69530P, Proceedings of SPIE - The International Society for Optical Engineering, vol. 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, Orlando, FL, United States, 17/03/08. https://doi.org/10.1117/12.777778

UXO detection, characterization and remediation using intelligent robotic systems. / Amer, Saed; Shirkhodaie, Amir; Rababaah, Haroun.
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII. 2008. 69530P (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6953).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - K-means clustering

KW - Neural networks

KW - Shape-based classifiers

KW - Template matching

KW - Texture-based classifiers

KW - Unexploded ordnance detection and remediation

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ER -

UXO detection, characterization and remediation using intelligent robotic systems

Abstract

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Conference

UN SDGs

Keywords

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