Towards human-robot collaboration in meat processing: Challenges and possibilities

Romanov, Dmytro; Korostynska, O.; Lekang, Odd Ivar; Mason, Alex

doi:10.1016/j.jfoodeng.2022.111117

Cited by 26 publications

(11 citation statements)

References 40 publications

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“…As a matter of fact, human-cobot collaboration is mainly tackled, as supported also by the results of the recent work by Aliev & Antonelli (2021). Indeed, these research works focused on the human-cobot interaction for various sectors (such as the meat production (Romanov et al, 2022) and civil engineering (Nagatani et al, 2021)) and operations (e.g., for productivity improvement (Mitrea & Tamas, 2018) and internal logistics (Donadio et al, 2018)). Most of the works focused on safety issues related to the use of collaborative robots in the same work environments as humans, by favouring the interaction (Bagheri et al, 2022), defining algorithms for trajectories and collisions prediction (Zhang et al, 2022).…”

Section: Content Analysis and Resultsmentioning

confidence: 95%

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

2023

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Smart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor.

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Section: Content Analysis and Resultsmentioning

confidence: 95%

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

2023

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“…However, on account of peculiarity of animal carcasses and the locations obtained from them, robotization and automation processes are a challenge for Meat 4.0. Despite this initial complexity, the development and implementation of robotization and automation are of special interest because the slaughter tasks and the secondary processing of the carcasses are labours that currently involve most of the manual work, which is repetitive and must be done at high speed [32]. Although it is true that in the case of slaughterhouses many processing operations (stunning, bleeding, scalding, plucking, skinning, evisceration, splitting, and cooling) are already successfully automated [57], in the secondary processing of meat, hardware and software must be developed so that robots can offer a flexible, scalable, compact, and profitable alternative in the production line [32].…”

Section: Industry 40 Technologies Applied For Meat Processing and Pre...mentioning

confidence: 99%

“…Nevertheless, the current trend rejects continuing to implement systems based on hard work performed by operators with the aim of providing a higher quality of work and reducing pathologies associated with it (musculoskeletal disorders). For this reason, technologies that combine human-robot collaboration are being investigated, creating the so-called CoBots, which would represent a very useful tool in the meat industry [32]. In addition, the use of CoBots would facilitate the inclusion of newly arrived operators since it would permit them to obtain a step-by-step approach to processing, achieving their training.…”

Section: Industry 40 Technologies Applied For Meat Processing and Pre...mentioning

confidence: 99%

“…In the meat industry, it was reported that the application of AI decreases costs by optimizing operations and improves profitability in meat processing plants [16]. An overview of recent developments and advances in human-robot collaboration in the red meat industry was given by Romanov et al [32]. The application of AR in slaughterhouses seemingly increases the production yield [33].…”

Section: Introductionmentioning

confidence: 99%

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Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Echegaray

Hassoun²,

Jagtap

et al. 2022

Applied Sciences

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Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

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“…Digital Object Identifier 10.1109/JSEN.2022.3208667 available to the sector, its use is often limited for economic reasons: simply put, today's equipment is too expensive for the majority of small-and medium-scale processers and lacks reasonable financial (and technical) scalability. With increased customization for both hardware and software, robots can offer a flexible, scalable, compact, and cost-effective production line alternative to older machinery that require large floor space, are difficult to adapt, and include higher maintenance costs [1].…”

Section: Introductionmentioning

confidence: 99%

Smart Knife: Integrated Intelligence for Robotic Meat Cutting

et al. 2022

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Automation is a key technology for a sustainable and secure meat sector in the future, both in terms of productivity and work environment. New robotic technologies, such as the so-called "meat factory cell," (MFC) aim to contribute to this goal, but they require new "smart" tools that provide sensor feedback, which enable robots to perform complex tasks. This article presents one such tool: the smart knife, which gives real-time feedback on its contact status with meat, as well as cutting depth. The tool and the system are described, and its operation evidenced via electromagnetic (EM) simulation using the Ansys High-Frequency Structure Simulator. Furthermore, the performance of the knife is validated using pork loin meat: in the worst case, knife is shown to have an error of 1.78% for contact detection, and a mean error of 7.66 mm (±1.45 mm) for depth detection. This article also presents brief discussion regarding eventual use of the knife as part of the MFC control system, in addition to future work to be performed.

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Towards human-robot collaboration in meat processing: Challenges and possibilities

Cited by 26 publications

References 40 publications

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms

Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry

Smart Knife: Integrated Intelligence for Robotic Meat Cutting

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