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

Background Meat is one of the main sources of protein in human nutrition. During recent years meat production volume has been showing significant growth worldwide. The total growth of red meat production is expected to show an 80% increase by 2029, according to the Organisation for Economic Co-operation Development (OECD). Such growth indicates the necessity for existing production line modernisation to satisfy future increased demand for meat products. Scope and approach This article critically reviews automation challenges for robotic applications in the meat industry, among those are heterogeneity of meat pieces and inconsistency of cutting trajectories that must be overcome to achieve the final quality product. It specifically focuses on human-robot collaboration (HRC) that could be applied in the meat industry to address these challenges. The paper elaborates on possible adaptation of HRC in meat industry, based on its achievements in other industries. Key finding and conclusions With increased customisation 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. However, in the case of red meat industry there are no off-the-shelf robotic solutions that can cover all the production steps in the secondary meat processing. Introducing collaborative robots into meat processing could help to promote higher standards in food safety and human-working conditions in the industry and make automation more affordable for smaller production plants.Towards human-robot collaboration in meat processing: Challenges and possibilitiespublishedVersio

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

Background: Meat is one of the main sources of protein in human nutrition. During recent years meat production volume has been showing significant growth worldwide. The total growth of red meat production is expected to show an 80% increase by 2029, according to the Organisation for Economic Co-operation Development (OECD). Such growth indicates the necessity for existing production line modernisation to satisfy future increased demand for meat products. Scope and approach: This article critically reviews automation challenges for robotic applications in the meat industry, among those are heterogeneity of meat pieces and inconsistency of cutting trajectories that must be overcome to achieve the final quality product. It specifically focuses on human-robot collaboration (HRC) that could be applied in the meat industry to address these challenges. The paper elaborates on possible adaptation of HRC in meat industry, based on its achievements in other industries. Key finding and conclusions: With increased customisation 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. However, in the case of red meat industry there are no off-the-shelf robotic solutions that can cover all the production steps in the secondary meat processing. Introducing collaborative robots into meat processing could help to promote higher standards in food safety and human-working conditions in the industry and make automation more affordable for smaller production plants

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

Supporting Semantic Capture during Kinesthetic Teaching of Collaborative Industrial Robots

Industrial robot systems being deployed today do not contain domain knowledge to aid robot operators in setup and operational use. To gather such knowledge in a robotic context requires mechanisms for entering and capturing semantic data. Such mechanisms would allow a system to gradually build a working vocabulary while interacting with the environment and operators, valuable for the bootstrapping system knowledge and ensuring the data collection over time. This paper presents a prototype user interface that assists the kinesthetic teaching mode of a collaborative industrial robot, allowing for the capture of semantic information while working with the robot in day-to-day use. Two modalities, graphical point-and-click and natural language, support capture of semantic context and the building of a working vocabulary of the environment while modifying or creating robot programs. A semantic capture experiment illustrates the approach