Model-based engineering of autonomous systems using ontologies and metamodels

Bermejo–Alonso, Julita; Hernández, Carlos; Sanz, Ricardo

doi:10.1109/syseng.2016.7753185

Cited by 10 publications

(14 citation statements)

References 12 publications

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“…This semantic model constitutes the first kind of knowledge for the CRC system to make decisions during the motion planning reasoning. Other conceptual models using ODEM (Bermejo-Alonso et al , 2016) can be obtained in a similar way in order to specify the system behavior in terms of the motion planning actions (e.g., avoid obstacles, resolve conflicts, etc. ), as shown in different UML activity and sequence diagrams by specializing OASys ontological concepts and relationships.…”

Section: Cloud Robotic Case Studymentioning

confidence: 99%

Ontologies for cloud robotics

Freitas¹,

Bermejo–Alonso

Khamis

et al. 2020

The Knowledge Engineering Review

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Cloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.

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Section: Cloud Robotic Case Studymentioning

confidence: 99%

Ontologies for cloud robotics

Freitas¹,

Bermejo–Alonso

Khamis

et al. 2020

The Knowledge Engineering Review

Self Cite

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show abstract

“…We use the same technologies, but to implement automatic diagnosis and reconfiguration of an autonomous underwater robot. The Ontology for Autonomous Systems (OASys) [3] is meant to describe and drive the entire life-cycle of autonomous systems, from engineering to operation, base on metamodelling and ontologies.…”

Section: Related Workmentioning

confidence: 99%

“…In a previous work [9] we demonstrated how the metamodelling approach in [3] can be used for self-adaptation at the mission level at runtime, going beyond component fault-tolerance. In this work, we advance in that roadmap with the use of ontologies to reason on the robot control architecture and its properties during the mission.…”

Section: Related Workmentioning

confidence: 99%

Meta-control and Self-Awareness for the UX-1 Autonomous Underwater Robot

Corbato

Milosevic

Olivares

et al. 2019

Advances in Intelligent Systems and Computing

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Autonomous underwater robots, such as the UX-1 developed in the UNEXMIN project, need to maintain reliable autonomous operation in hazardous and unknown environments. Because of the lack of any kind of real-time communications with a human operated command and control station, the control architecture needs to be enhanced with mission-level self-diagnosis and self-adaptation properties an additional provided by some kind of supervisory or "metacontrol" component to ensure its reliability. In this paper, we propose an ontological implementation of such component based on Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). The solution is based on an ontology of the functional architecture of autonomous robots, which allows inferring the effects of the performance of its constituents components in the functions required during the robot mission, and generate the reconfigurations needed to maintain operation reliably. The concept solution has been validated using a hypothetical set of scenarios implemented in an OWL ontology and an OWLAPI-based reasoner, which we aim at validating by integrating the metacontrol reasoning with a realistic simulation of the underwater robot.

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“…Knowledge in the form of explicit models supported by ontologies and metamodels grounds advanced and robust robot capabilities. Having an autonomous systems reference ontology [8] and related patterns becomes a significant advantage to obtain the autonomous system's models [7], compared to developing them in an ad-hoc or case by case manner.…”

Section: Ontologies To Support Model-based Metacontrolmentioning

confidence: 99%

“…This model will be converted in the OM Model, so it has to be captured with a conceptualization that can be mapped to TOMASys elements. Applying Model-Based Systems Engineering with the OASys-driven Engineering Methodology [7], which is based on the same underlying ontology than TOMASys, facilitates this.…”

Section: )mentioning

confidence: 99%

A self-adaptation framework based on functional knowledge for augmented autonomy in robots

Hernández

Bermejo–Alonso

Sanz

2018

ICA

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Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust operation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is required in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robot's controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robot's mission with the robot's architecture, and it is generated during the robot's development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robot's functional model to adapt the control architecture to recover from a sensor failure.

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Model-based engineering of autonomous systems using ontologies and metamodels

Cited by 10 publications

References 12 publications

Ontologies for cloud robotics

Ontologies for cloud robotics

Meta-control and Self-Awareness for the UX-1 Autonomous Underwater Robot

A self-adaptation framework based on functional knowledge for augmented autonomy in robots

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