Skill-based instruction of collaborative robots in industrial settings

Schou, Casper; Andersen, Rasmus; Chrysostomou, Dimitrios; Bøgh, Simon; Madsen, Ole

doi:10.1016/j.rcim.2018.03.008

Cited by 119 publications

(72 citation statements)

References 12 publications

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“…Rather than remove the need for human interventions, the current trends see a change of tasks and the persons involved in daily operation procedures. Traditional robot programming has been the role of engineers, but in order to exploit reduced change-over times and increased efficiency, the task of programming is moving to operators on the shop floor (Schou et al, 2018). This has both monetary benefits (decreasing the need for high-wage engineering), it simultaneously allows the operator to channel valuable process knowledge and experience into the instruction.…”

Section: Technological Dimensionsmentioning

confidence: 99%

Collaborative Robots: Frontiers of Current Literature

Knudsen

Kaivo‐oja

2020

Journal of Intelligent Systems: Theory and Applications

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Collaborative Robots (Cobots) play an increasing role in the advanced manufacturing landscape. The cobot market is rapidly expanding, and the academic literature is similarly growing. This paper presents a short overview of current cobotic trends and futures frontiers of the cobot development with particular emphasis on the role of cobots within the Industry 4.0-paradigm and within global megatrends. Since there is a strange, but distinct lack of literature reviews on the topic of collaborative robotics, the paper provides novel and valuable insights. In highlighting current frontiers, the paper also illustrates potential developments of future human-robot interaction.

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Section: Technological Dimensionsmentioning

confidence: 99%

Collaborative Robots: Frontiers of Current Literature

Knudsen

Kaivo‐oja

2020

Journal of Intelligent Systems: Theory and Applications

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“…Research mainly focuses on how new skills can be implemented and how a manufacturing task can be programmed through sequences of skill invocations [11]- [14] or how production systems can be changed in order to gain or adjust skills, or to compensate the failure of parts of the system [15]. These approaches have in common that they are tool-centric.…”

Section: Related Workmentioning

confidence: 99%

An Ontology-based Metamodel for Capability Descriptions

Weser

Bock

Schmitt

et al. 2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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This paper presents an approach to describe abilities of manufacturing resources by a formal description of capabilities using Semantic Web technologies. A hierarchical ontology architecture is proposed to represent, publish, and extend knowledge on capabilities for different application domains and use cases. Furthermore, the paper describes patterns of how the underlying formal logic can be used in taxonomy modeling and the inference of implicit capability facts. The usability and performance of the approach was validated by formalizing capability knowledge of related work and evaluated in benchmarking a prototypical implemented tool for managing and querying catalogs of resources and their capabilities. The proposed concept is intended to be used as a foundation for a future multi-layered feasibility checking, which evaluates the compatibility of resources and their offered skills with the requirements of manufacturing tasks at symbolic and subsymbolic levels. Extended evaluations might be based on parameters, analytics, simulation, and other means.

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“…The workcell operator gets access to these functionalities and performs the integration process. Modularity in the process plans allows a high degree of reuse and outer parametrization like it is commonly used in skill architectures [27], [28], [29]. The structure of the AML model is presented in Fig.…”

Section: Plug Plan and Producementioning

confidence: 99%

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

Wojtynek

Steil²,

Wrede

2019

Künstl Intell

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The transformation of today's manufacturing lines into truly adaptive systems facilitating individualized mass production requires new approaches for the efficient integration, configuration and control of robotics and automation components. Recently, various types of Plug-and-Produce architectures were proposed that support the discovery, integration and configuration of field devices, automation equipment or industrial robots during commissioning or even operation of manufacturing systems. However, in many of these approaches, the configuration possibilities are limited, which is a particular problem if robots operate in dynamic environments with constrained workspaces and exchangeable automation components as typically required for flexible manufacturing processes. In this article, we introduce an extended Plug-and-Produce concept based on dynamic motion planning, co-simulation and a collaborative human-robot interaction scheme that facilitates the quick adaptation of robotics behaviors in the context of a modular production system. To confirm our hypothesis on the efficiency and usability of this concept, we carried out a feasibility study where participants performed a flexible workcell setup. The results indicate that the assistance and features for planning effectively support the users in tasks of different complexity and that a quick adaption is indeed possible. Based on our observations, we identify further research challenges in the context of Plug, Plan and Produce applied to smart manufacturing.

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Skill-based instruction of collaborative robots in industrial settings

Cited by 119 publications

References 12 publications

Collaborative Robots: Frontiers of Current Literature

Collaborative Robots: Frontiers of Current Literature

An Ontology-based Metamodel for Capability Descriptions

Plug, Plan and Produce as Enabler for Easy Workcell Setup and Collaborative Robot Programming in Smart Factories

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