Deep Learning-Based Trajectory Planning for Reflective Workpiece Alignment in Robotic Welding
Year: 2025
Type: article
Abstract: Recent advancements in robotic welding and vision-aided automation have transformed industrial processes by providing high precision, rapid detection, and adaptability compared to traditional methods. This study introduces a trajectory generation approach based on Deep Iterative Matching, leveraging RGB-D data from industrial cameras (Zivid 2) to calculate welding paths, initial positions, and correct placement errors. A real dataset of aluminum workpieces was created to train deep neural networks (DNNs) to handle measurement corruption caused by reflections. By integrating intelligent solutions, this research aims to improve automation reliability, repeatability, and efficiency, advancing welding technologies in complex industrial environments.
Source: IFAC-PapersOnLine
Institution: Norwegian University of Science and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414567767