Physically Guided Multiscale Visual Servoing Robot for Automatic Detection of Large Heterogeneous Composite Structure

Abstract

Optical pulsed thermography (OPT) is an effective method for detecting defects in composite materials and has been widely applied in aerospace and other industries; however, when inspecting large-scale heterogeneous composite materials, conventional fixed detection platforms (which are stationary and unable to change their ground position) cannot automatically and accurately detect defects within a 3-D model in a single operation. This article proposes a multiscale visual servo detection framework based on a mobile detection platform, transitioning from a fixed detection platform with a mobile composite material to a fixed composite material with a mobile detection platform. The defect detection process for large-scale heterogeneous composites is divided into three scales: 1) rapid positioning of composite materials using process learning; 2) precise positioning to minimize system errors and enhance 3-D model accuracy through self-learning; and 3) defect detection via infrared measurement field division. The proposed framework enables fully automatic defect detection, precise defect mapping, and accurate 3-D modeling of large heterogeneous composites. Compared to traditional fixed detection platforms, this approach significantly improves efficiency by detecting large-scale heterogeneous composites in a single operation, achieving high-performance defect detection and enhanced 3-D model accuracy.