Ontology-Based Knowledge Representation for Increased Skill Reusability in Industrial Robots

Topp, Elin Anna; Stenmark, Maj; Ganslandt, Alexander; Svensson, Andreas; Haage, Mathias; Malec, Jacek

doi:10.1109/iros.2018.8593566

Cited by 16 publications

(16 citation statements)

References 13 publications

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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: 87%

“…Many approaches follow the product-processresources paradigm (PPR) [6], in which products [7] and product designs [8], production processes [9], and manufacturing resources [10] are modeled. 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.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Discussionmentioning

confidence: 87%

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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“…True (e.g., maximisation of human convenience, or for a restaurant runner task, the time at which a new client approaches a table serves as a deadline for cleaning the table) The types of jobs/tasks can have dedicated schedule parameters False, processes have priorities which can be directly compared True, various da class agents can compute different schedule parameters which can be transformed and compared with each other by tha class agent (e.g., object transportation uses the shortest travel distance, and human guidance maximises human convenience) Hard restriction of deadlines Available Depends on the robot application: for social and service robots, the deadlines can be fuzzy 4) evaluate metrics for task scheduling quality evaluation, 5) provide an adaptation mechanism for the scheduling algorithm to enable learning of the priorities of tasks based on the user's intentions [71], [72], 6) design an ontology for the robot task harmonisation problem that facilitates the configuration of the schedule parameters (according to robot skill reconfiguration [73]), 7) conduct tests with end users within the INCARE project [74], and 8) integrate our architecture with one of the formally defined task models featured with consistent language for scheduling algorithm specification and planning method [59].…”

Section: Falsementioning

confidence: 99%

Scheduling of a Robot’s Tasks With the TaskER Framework

Dudek

Winiarski

2020

IEEE Access

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Robots, in contrast to typical computational systems, affect the physical environment directly. Therefore, other assumptions must be considered for task management procedures in these system types. Robots coexist with humans in the environment and act upon potentially dangerous objects (e.g., a cooker); hence, extra safety procedures in robot task harmonisation must be ensured. Additionally, an algorithm that schedules tasks for a robot and optimises the robot's operation needs to consider the robot motion time, dynamics of the physical processes, changes in the robot user preferences and changes in the environment made by other habitants. In this article, we investigate the problem of switching between various independent tasks safely and state requirements for a control system resolving it. The tasks are uploaded to a store, launched on a robot at the user's request (similar to smartphone applications) and scheduled following a configurable algorithm. Furthermore, we design a model of systems satisfying the requirements. The systems are structured with agents of different classes. We propose a task-switching procedure and dedicated states of the finite-state machines describing the operation of the agents. Finally, we present a TaskER framework implementing the model, and we verify the model through the execution of an exemplary system in scenarios showing the benefits of the model implementation. As a result of our approach application, the robot tasks can be safely interrupted, postponed, resumed, and potential danger (e.g., leaving a cooker on for a long time) can be minimised.

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“…In these projects, the ontology has been used to enhance cognitive abilities of robots that are required to plan and execute assembly tasks. The core ontology has been reorganized after the initial release (Jacobsson et al , 2016), and new case studies on robot programming support (Topp & Malec, 2018) and skill reusability in industrial scenarios (Topp et al , 2018) have been developed.…”

Section: Ontologies To Support Robot Autonomymentioning

confidence: 99%

“…First, ontologies are used to support the kinesthetic teaching so that the learned primitives are sematically represented as skills (Stenmark et al , 2018). Second, (Topp et al , 2018) discuss how the representation of already learned robot’s skills enhances the transfer of knowledge from one robot to others.…”

Section: Ontologies To Support Robot Autonomymentioning

confidence: 99%

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

Olivares-Alarcos

Beßler

Khamis

et al. 2019

The Knowledge Engineering Review

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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.

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Ontology-Based Knowledge Representation for Increased Skill Reusability in Industrial Robots

Cited by 16 publications

References 13 publications

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

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

Scheduling of a Robot’s Tasks With the TaskER Framework

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

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