open access publication

Article, 2024

RoBUTCHER: A novel robotic meat factory cell platform

The International Journal of Robotics Research, ISSN 0278-3649, 1741-3176, 10.1177/02783649241234035

Contributors

Mason, Alex 0000-0002-0147-9135 [1] [2] De Medeiros Esper, Ian 0000-0002-8172-1621 [1] Korostynska, Olga 0000-0003-0387-6609 [1] [3] Cordova-Lopez, Luis Eduardo 0000-0002-1378-7100 [1] Romanov, Dmytro 0000-0002-4077-8306 [1] Pinceková, Michaela [1] Bjørnstad, Per Håkon [2] Alvseike, Ole Arne 0000-0002-1748-3470 [2] Popov, Anton Oleksandrovich 0000-0002-1194-4424 [4] [5] [6] Smolkin, Oleh [4] [5] Manko, Maksym [5] [6] Christensen, Lars Bager [7] Takács, Kristóf 0000-0001-5417-6026 [8] Haidegger, Tamás [8]

Affiliations

  1. [1] Norwegian University of Life Sciences
  2. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];
  3. [2] Research and Development Department, Norwegian Meat and Poultry Research Center (Animalia AS), Oslo, Norway
  4. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];
  5. [3] Department of Mechanical, Electrical and Chemical Engineering, OsloMet, Oslo, Norway
  6. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];
  7. [4] Ukrainian Catholic University
  8. [NORA names: Ukraine; Europe, Non-EU];
  9. [5] Ciklum Data and Analytics, Kyiv, Ukraine
  10. [NORA names: Ukraine; Europe, Non-EU];

Abstract

Automation is critically important for sustainability in meat production, where heavy reliance on human labour is a growing challenge. In this work, a novel robotic Meat Factory Cell (MFC) platform presents the opportunity for unconventional automation in pork meat processing, particularly abattoirs. Instead of following line-based approaches, which are the main option today, it uses robotics and Artificial Intelligence (AI) to perform complex cutting and manipulation operations on entire unchilled pork carcasses, with awareness of biological variation and deformation. The long-term goal of the MFC is to take a pork carcass as an input and produce seven primal outputs: hams, shoulders, saddle, belly and entire organ set. However, the MFC platform is under continuous development – therefore, this paper aims to demonstrate it through a specific use-case: shoulder removal. The system is evaluated based on data from testing and development sessions (June–November 2022), with a total of 34 attempted shoulder removals. Data regarding the MFCs’ ability to handle variation, in addition to success rate and process timing models are presented. Qualitative feedback from skilled butchers is also discussed. The authors propose that, as well as technical development of the platform, it is important to consider new ways of comparing unconventional systems with their conventional counterparts. Innovative manufacturing systems have more to offer than raw speed and volume; traits such as flexibility, robustness and scalability – particularly economic scalability – should play a prominent role. Future legislation and standards must also encourage innovation rather than hinder innovative robotics solutions.

Keywords

Artificial, Hamming, Meat Factory Cell, abattoir, ability, approach, artificial intelligence, authors, automation, awareness, belly, biological variation, butchers, carcass, cells, complex cuts, continuous development, conventional counterparts, counterparts, cutting, data, deformation, development, development sessions, economic scalability, factory cells, feedback, flexibility, goal, human labor, innovation, input, intelligence, labor, legislation, line-based approach, long-term goal, manipulation, manipulation operations, manufacturing systems, meat, meat processing, meat products, model, operation, organization, organization setting, output, platform, pork, pork carcasses, process, production, qualitative feedback, rate, raw speed, removal, robot, robotic solutions, robustness, scalability, sessions, sets, shoulder, skilled butchers, solution, speed, standards, success, success rate, sustainability, system, technical developments, test, time model, traits, unconventional systems, use-cases, variation, volume

Funders

  • The Research Council of Norway
  • European Commission

Data Provider: Digital Science