An Indoor Navigation Ontology for Production Assets in a Production Environment

Scholz, Johannes; Schabus, Stefan

doi:10.1007/978-3-319-11593-1_14

Cited by 23 publications

(39 citation statements)

References 28 publications

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“…This approach may need to develop with OWL ontology. Scholz, J. and Schabus, S. adopted an ontology to support autonomous indoor navigation in the production environment [16]. This research used RFID and ultrasound technology to support autonomous navigation in the indoor space and develop a tracking system called LotTrack.…”

Section: Related Workmentioning

confidence: 99%

Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Khruahong

Kong

Sandrasegaran

et al. 2017

2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE)

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Abstract-Indoor navigation will become an important application on a smartphone for Location-Based Service (LBS). An indoor navigation system should work under normal circumstances and during emergencies, such as fires, during a building power shut down, alarm, etc. The LBS should be able to help users find the best exit route to the outside of the building under all circumstances and with high reliability. In this research, we develop an indoor ontology model for indoor navigation. This ontology model defines the indoor environment attributes such as location nodes, and connection points. The location nodes with the location information allow navigation in the indoor environment. Connection points are able to separate the map zones and the building floors into a "Map sheet." This ontology approach allows the LBS works in both normal circumstances and emergencies. This model provides a reliable indoor navigation system for LBS.

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Section: Related Workmentioning

confidence: 99%

Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Khruahong

Kong

Sandrasegaran

et al. 2017

2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE)

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“…Scholz and Schabus [8] created an indoor navigation ontology for the movement of production assets in a production environment, to support autonomous navigation in the indoor space. Production assets pass through steps of a workflow, and for each step the suitable equipment is found and the best route to the equipment is calculated.…”

Section: Related Workmentioning

confidence: 99%

Indoor Navigation for Motion Disabled Persons in Medical Facilities

2017

APH

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“…Currently, there are a number of initiatives subsumed under the umbrella of smart manufacturing or Industry 4.0 that strive to increase the efficiency of manufacturing processes. In this context, Geoinformation may contribute in terms of modeling indoor space [3] or decision support for manufacturing purposes [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…2017, 6, 280 2 of 18 of initiatives subsumed under the umbrella of smart manufacturing or Industry 4.0 that strive to increase the efficiency of manufacturing processes. In this context, Geoinformation may contribute in terms of modeling indoor space [3] or decision support for manufacturing purposes [4][5][6]. The universe of discourse (UoD) under review in this paper is a complex and highly flexible manufacturing environment of a semiconductor company, described in detail by [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, Geoinformation may contribute in terms of modeling indoor space [3] or decision support for manufacturing purposes [4][5][6]. The universe of discourse (UoD) under review in this paper is a complex and highly flexible manufacturing environment of a semiconductor company, described in detail by [3,4]. To illustrate the complexity in the manufacturing line, there are several hundred production steps necessary to manufacture a single microchip.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Affordance-Based Ad-Hoc Suitability Network for Indoor Manufacturing Transportation Processes

Scholz

Schabus

2017

IJGI

Self Cite

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Abstract:In manufacturing companies, productivity and efficiency are the main priorities, besides an emphasis on quality issues. The outcome of this research contributes to increasing production quality and efficiency in manufacturing. The article deals with indoor manufacturing environments and the transportation processes of production assets-referred to as smart transportation. The authors modelled the objects present in the indoor manufacturing environment with ontologies including their affordances and spatial suitability. To support flexible production and dynamic transportation processes have to be tailored towards the 'needs' of the production asset. Hence, the authors propose an approach utilizing an ad-hoc suitability network to support the "optimal" path computation for transportation processes. The objective is to generate a graph for routing purposes for each individual production asset, with respect to the affordances of the indoor space for each production asset, and measurements of a sensor network. The generation of the graph follows an ad-hoc strategy, in two ways. First, the indoor navigation graph is created exactly when a path needs to be found-when a production asset shall be transported to the next manufacturing step. Secondly, the transportation necessities of each production asset, as well as any disturbances present in the environment, are taken into account at the time of the path calculation. The novelty of this approach is that the development of the navigation graph-including the weights-is done with affordances, which are based on an ontology. To realize the approach, the authors developed a linked data approach based on manufacturing data and on an application ontology, linking the indoor manufacturing environment and a graph-based network. The linked data approach is finally implemented as a spatial graph database containing walkable corridors, production equipment, assets and a sensor network. The results show the optimal path for transportation processes with respect to affordances of the indoor manufacturing environments. An evaluation of the computational complexity shows that the affordance-based ad-hoc graphs are thinner and thus reduce the computational complexity of shortest path calculations. Hence, we conclude that an affordance-based approach can help to decrease computational efforts for calculating "optimal" paths for transportation purposes.

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An Indoor Navigation Ontology for Production Assets in a Production Environment

Cited by 23 publications

References 28 publications

Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Indoor Navigation for Motion Disabled Persons in Medical Facilities

Towards an Affordance-Based Ad-Hoc Suitability Network for Indoor Manufacturing Transportation Processes

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