A System Architecture for Constraint-Based Robotic Assembly with CAD Information

Arbo, Mathias Hauan; Pane, Yudha; Aertbeliën, Erwin; Deere, Wilm

doi:10.1109/coase.2018.8560450

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…There are of course other aspects beyond safety which distinguishes how robots are being used in smart factories. Vision and CAD aided robot planning and control can help avoid time-consuming manual programming of robots, see, e.g., [20,21]. Learning by demonstration can also relieve for the need for manual programming, e.g., through the use of dynamic motion primitives used to parameterize robotic motions [22].…”

Section: Robots In Smart Factoriesmentioning

confidence: 99%

Trends in Smart Manufacturing: Role of Humans and Industrial Robots in Smart Factories

et al. 2020

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Purpose of Review This paper provides an overview of the role of humans and robots in smart factories, their connection to Industry 4.0, and which progress they make when it comes to related technologies. Recent Findings The current study shows that a decade was not enough to provide a reference implementation or application of Industry 4.0, like smart factories. In 2011, Industry 4.0 was mentioned for the first time in the scientific community. Industry 4.0 arrived with many new enabling technologies and buzzwords, e.g., Internet of Things (IoT), Cyber-Physical Systems (CPS), and Digital Twins (DT). Summary This paper first defines smart factories and smart manufacturing in relation to the role of humans and robots. Followed by an overview of selected technologies in smart factories. Concluded by future prospects and its' relation to smart manufacturing.

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Section: Robots In Smart Factoriesmentioning

confidence: 99%

Trends in Smart Manufacturing: Role of Humans and Industrial Robots in Smart Factories

et al. 2020

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“…12: An automotive supplier's shop floor analytics dashboard showing the maximum measured fuse mounting forces in Z in the end effector frame for each PickAndPlaceTask in one execution of process P1 (top) and across different assemblies for slot 9 (bottom). For the mounting of mini fuses (11)(12)(13)18) higher maximum Z forces were measured than for regular ATO fuses (3-5, 9, 14).…”

Section: Discussionmentioning

confidence: 88%

“…The combination of skills and explicit knowledge representation is often used to reduce the complexity of programming production systems [9], [11], [12]. In [13], a CAD-based instruction of assembly tasks is described. The authors suggest a system architecture, where geometric constraints are specified on an application layer to describe assembly steps.…”

Section: Related Workmentioning

confidence: 99%

Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories

Perzylo

Kessler

Profanter

et al. 2020

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

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Digital transformation efforts in manufacturing companies bear the potential to reduce product costs and increase the flexibility of production systems. The semantic integration of data and information along the value chain enables the automated interpretation of interrelations between its different aspects such as product design, production process and manufacturing resources. These interrelations can be used to automatically generate semantic process descriptions and execute corresponding robot motions. An initial one-time effort to model the required knowledge of a particular application domain can make the manufacturing of high-variant products in small batches or even lot size one production more efficient. This paper introduces a knowledge-based digital engineering concept to automate engineering and production activities without human involvement. The concept was integrated and evaluated in a physical robot workcell where automotive fuse boxes are autonomously fitted with different fuse configurations.

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“…The former deals with finding the suitable skills from the provided library for a given assembly task. To this end, we extend our initial work from [3] by adding a reasoning module implemented in Prolog to search through the ontology for the appropriate skills. Once a skill is chosen, it needs contextdependent parameters in order to be executable.…”

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confidence: 99%

“…In our previous work [3], the mapping from CAD-level specification to concrete skills was done statically without involving any reasoning capability. This limits the reusability of the skills and puts more programming load on the user.…”

mentioning

confidence: 99%

A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

Pane

Arbo

Aertbeliën

et al. 2020

IEEE Trans. Automat. Sci. Eng.

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Specifying assembly tasks in CAD-level is a promising approach to intuitively program complex robot skills. In this paper, a three-layered system architecture is presented to generate sensor-based robot skills from an assembly task instance. The architecture consists of an application layer where the user instantiates assembly tasks by specifying CAD constraints between geometric primitives pairs. A process layer infers the most suitable robot skills and their appropriate parameters. This inference is made possible by reasoning on a knowledge database represented as an ontology. The ontology contains semantic models of relevant classes such as tasks, skills, and geometric primitives as well as the relations between them. A control layer executes the sensor-based skills in real time using the eTaSL programming framework. A software implementation for the three layers is presented. The application layer is implemented in FreeCAD while the process layer consists of an OWL ontology, a Prolog-based reasoner and fuzzy inference to correctly select the skill and generate its parameters. In the control layer, the instantiated eTaSL skills execute the assembly tasks by sending an optimized control command to the robot. The system is validated on two challenging assembly cases with two distinct robot types, thus demonstrating the system's capability across different scenarios.Note to Practitioners-The widespread use of CAD models for describing parts assembly has motivated the research community to create systems that automatically generate robot programs satisfying the assembly goal. While most of the existing literature focuses on generating the assembly sequence, this paper deals with the aspect of translation from CAD-level assembly specification to executable robot motion, also called skills. The paper systematically addresses the problem by dividing it into different layers and solving them separately. Parameters that influence the successful execution of an assembly task are identified and categorized into application-related and processrelated parameters. Different inference techniques are employed to address each parameter category. Experimental results show that the proposed system is able to successfully generate and execute robot skills for assembly scenarios of an air compressor and an electric motor.

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A System Architecture for Constraint-Based Robotic Assembly with CAD Information

Cited by 9 publications

References 17 publications

Trends in Smart Manufacturing: Role of Humans and Industrial Robots in Smart Factories

Trends in Smart Manufacturing: Role of Humans and Industrial Robots in Smart Factories

Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories

A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills

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