AR for conveying defects information to production line operators with collaborative robot assistance
Reference persons FABRIZIO LAMBERTI
Research Groups GR-09 - GRAphics and INtelligent Systems - GRAINS
Thesis type THESIS IN COLLAB. W/ A COMPANY
Description In modern production processes, the efficient detection and management of defects are critical for ensuring high-quality output and optimizing manufacturing operations. The advent of automation and advanced computer vision techniques has enabled the automatic detection of defects in real-time. However, conveying this vital information to production line operators in a clear, intuitive, and timely manner remains a challenge. This thesis proposal aims to investigate the application of Augmented Reality (AR) technology, enhanced by Collaborative Robot (Cobot) assistance, as a means of conveying automatically detected defects information to production line operators. The research will involve the integration of the output from computer vision algorithms and defect detection methodologies within a production line. Cobots will be instrumental in assisting operators during the tasks of defect detection and correction. By utilizing AR technology, the proposed investigation seeks to overlay defect-related information, such as visual cues, guidance, and instructions, directly onto the physical production environment in real-time, with the Cobot playing a pivotal role in enhancing the operator's capabilities and accuracy during defect management tasks. This approach aims to enhance operators' situational awareness, enable rapid decision-making, and facilitate timely defect resolution. The research collaboration will actively engage industry partners, ensuring the practicality and relevance of the findings. Moreover, the project will be in alignment with the overarching objectives of the MANAGE 5.0 project, with support from the Ministry of Enterprises and Made in Italy (MISE) and the National Recovery and Resilience Plan (PNRR).
See also http://grains.polito.it/work.php
Deadline 26/08/2024 PROPONI LA TUA CANDIDATURA