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.

Automatic Visual Recognition of Unexploded Ordnances Using Supervised Deep Learning

“…One of the earliest works dealt with UXO detection from images that were captured by a hypothetical robotic system. To discriminate the UXOs from background clutter, foreground segmentation via adaptive thresholding was performed followed by color, texture and shape-based classification [2,29]. To further reduce the false positive rate a decision-level fusion schema was proposed.…”