TY - GEN
T1 - UXO detection, characterization and remediation using intelligent robotic systems
AU - Amer, Saed
AU - Shirkhodaie, Amir
AU - Rababaah, Haroun
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Color-based classifiers
KW - K-means clustering
KW - Neural networks
KW - Shape-based classifiers
KW - Template matching
KW - Texture-based classifiers
KW - Unexploded ordnance detection and remediation
UR - http://www.scopus.com/inward/record.url?scp=44349122316&partnerID=8YFLogxK
U2 - 10.1117/12.777778
DO - 10.1117/12.777778
M3 - Conference contribution
AN - SCOPUS:44349122316
SN - 9780819471444
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
T2 - Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Y2 - 17 March 2008 through 20 March 2008
ER -