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SHEREC: Safe, Healthy and Environmental Ship Recycling

Improve occupational health and safety conditions within the ship recycling industry. By leveraging state-of-the-art robotics, computer vision, and artificial intelligence systems, we aspire to revolutionize traditional methodologies while promoting environmental stewardship and professional standards.

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Photo: Sven Hansche/Shutterstock

Background

Ship recycling refers to the disassembly of a ship in recycling facilities, as well as the storage and processing of these materials to reuse them. End-of-life ships can contain various amounts of toxic materials in their structure, which need to be properly located, identified, removed, and disposed of. This is why, in addition to work-related diseases due to toxic substances, the unacceptably high levels of fatalities and injuries at current shipbreaking yards make ship recycling one of the most dangerous occupations in the world.

Improve the occupational health and safety conditions

The main objective of SHEREC is to enable the involvement and adoption of innovative robotics, data, and AI systems into the ship recycling industry to significantly improve the occupational health and safety conditions in this industry and to prevent contamination from hazardous materials at both occupational and environmental levels. Specifically, SHEREC will

  1. semi-automate the preparation process in ship recycling using an AI-powered drone that inspects the interior of the ship to locate hazardous materials on the ship
  2. create an automated ship recycling plan using a digital twin of the ship and AI-based planning methods
  3. automate the cutting and paint removal processes in the ship recycling process using two innovative mobile robotic systems that can work autonomously or via tele-operation.

Ship interior inspection and hazardous material detection

SINTEF will develop a a Simultaneous Localization and Mapping (SLAM) solution for autonomous drone navigation and detection of hazardous materials, and determination of their location in correspondence with an Inventory of Hazardous Material (IHM) report. 

The Consortium

The consortium comprises 15 partner organizations from both the research and industrial sectors.


 

See also Deep learning on images, videos and 3D data

Key facts

Project duration

2024 - 2027

Funding

The European Union

Partners

15 included:

  • HKTM (Coordinator): Hidropar Hareket Kontrol Teknolojileri Merkezi Sanayi ve Ticaret Anonim Şirketi
  •  LUT: Lappeenranta University of Technology
  •  UPB: Universität Paderborn
  •  UPM: Universidad Politécnica de Madrid
  • AVSAR: Avşar Gemi Söküm Sanayi ve Dış Ticaret Limited Şirketi
  • NTNU: Norges teknisk-naturvitenskapelige universitet 

Project type

EU Horizon Innovation Actions - Horizon IA