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A Vision-driven Robotic Dynamic Grasping System for Anomaly Objects in Dynamic Production Scenarios

Project description

Robotic dynamic grasping system to quickly detect anomalous objects

The advanced robotic systems market is growing worldwide. For EU industries to remain competitive, they need to pursue sustainable growth, smart automation, and a circular economy. Currently, robotic systems are used for picking and sorting, while humans still handle defective products. The few datasets available and defect types has limited research in anomaly and grasp detection. Supported by the Marie Skłodowska-Curie Actions programme, the VRDGS project is addressing these challenges. It is developing a solution that enables the robotic gripper to quickly identify anomalous objects and remove them without causing downtime, ensuring production efficiency and quality.

Objective

In 2021, global manufacturing activity had rebounded to pre-pandemic levels, accounting for approximately 17% of worldwide gross domestic product on average, and the EU contributing 14.9%, solidifying its position as the region’s most economically significant industrial sector . However, the implement of advanced robotic systems has shown an annual global increment of 12% with the most active being in the industrial sector , which is expected to reach USD 60,562 million by 2030 . To sustain its position and enhance its manufacturing competitiveness, EU manufacturing industries must advance Green Deal-driven sustainable industrial growth , accelerate smart automation under Industry 5.0 and enable circular economy transitions , while upholding stringent product quality and safety standards. Thus, developing advanced, production-challenge-tailored robotic systems has grown ever more critical. In current industrial settings, vision-driven robotic grasping models are predominantly deployed for picking and sorting tasks on production lines , whereas defective products are typically identified by vision systems but still often removed by human laborers. Academically, research in anomaly detection (AD) and grasping pose detection (GPD) has been progressing independently due to the limited industrial datasets, fewer defect types and temporal dependencies. Against this backdrop, the Vision-driven Robotic Defective Grasping System (VRDGS) emerges as a solution designed to address these challenges head-on. The robotic griper is required to quickly identify anomaly objects and remove them without downtime, ensuring production efficiency and quality.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Net EU contribution
€ 260 347,92
Address
TRINITY LANE THE OLD SCHOOLS
CB2 1TN CAMBRIDGE
United Kingdom
Region
East of England East Anglia Cambridgeshire CC
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data