Cognition-enabled Framework for Mixed Human-Robot Rescue Teams

Yazdani, Fereshta; Kazhoyan, Gayane; Bozcuoglu, Asil Kaan; Haidu, Andrei; Balint-Benczedi, Ferenc; Bebler, Daniel; Pomarlan, Mihai; Beetz, Michael

doi:10.1109/iros.2018.8594311

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…In the last 10 years, based on a keyword search of ontologyrobotics-reliability-diagnosis, and on a survey carried out by IEEE-SA P1872.2 Standard for Autonomous Robotics (AuR) Ontology, there has been around seventy publications relating to ontology and autonomy for robotics. Within these publications several authors have applied ontology as a knowledge-based scheme (Knowledge Representation), within a system to support robotics autonomy, such as SMERobotics [35], KnowRob 2.0 [36], CARESSES [37], open-EASE [38], ORO [39,40], and SIARAS [41]. These prior works focused on one or more of the cognitive functionalities in a robotic system, i.e., reasoning scope.…”

Section: Assessment Of Robotic Ontology Applied To Reliabilitymentioning

confidence: 99%

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

et al. 2021

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A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.

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Section: Assessment Of Robotic Ontology Applied To Reliabilitymentioning

confidence: 99%

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

et al. 2021

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“…In the context of RaaS, as proposed by several researchers (Quintas et al, 2011Bozcuoglu et al, 2018;Yazdani et al, 2018), robots can be seen as serviceproviders, transparently sharing their resources with other robots. This resource sharing depends on a match between what is being demanded and what are the robots' capabilities to configure and accomplish a service that meets the demands.…”

Section: Cloud Robotics At a Glancementioning

confidence: 99%

“…The rationale behind CR is the possibility to take advantage of the benefits of sharing data and processing resources provided by cloud-based systems in order to enhance and to enlarge the power and the usefulness of robotic systems, according to their specific needs (Quintas et al, 2011). When using the cloud, a robot could improve its capabilities of image understanding, language translation, speech recognition (Zinchenko et al, 2017), path planning, and/or 3D mapping (Guizzo, 2011).…”

Section: Introductionmentioning

confidence: 99%

Ontological concepts for information sharing in cloud robotics

Freitas

Olszewska

Carbonera

et al. 2020

J Ambient Intell Human Comput

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Recent research and developments in Cloud Robotics (CR) require appropriate knowledge representation to ensure interoperable data, information, and knowledge sharing within cloud infrastructures. As an important branch of the Internet of Things (IoT), these demands to advance it forward motivates academic and industrial sectors to invest on it. The IEEE 'Ontologies for Robotics and Automation' Working Group (ORA WG) has been developing standard ontologies for different robotic domains, including industrial and autonomous robots. The use of such robotic standards has the potential to benefit the Cloud Robotic Community (CRC) as well, supporting the provision of ubiquitous intelligent services by the CR-based systems. This paper explores this potential by developing an ontological approach for effective information sharing in cloud robotics scenarios. It presents an extension to the existing ontological standards to cater for the CR domain. The use of the new ontological elements is illustrated through its use in a couple of CR case studies. To the best of our knowledge, this is the first work ever that implements an ontology comprising concepts and axioms applicable to the CR domain.

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“…The system stores knowledge about the cultural information of the users, which is used by the robot's finite state machine to control the interaction. KnowRob 2.0 was used and extended in a research project that was concerned with mixed humanrobot rescue tasks (Yazdani et al, 2018). The scenario is that a team of different robots has to locate an avalanche victim in hilly terrain where, first, a flying robot scans the area, and then, after the victim was found, the robot communicates the particular location, and an image captured by its camera to the human operator, and to the other robots.…”

Section: Interaction and Communicationmentioning

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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Cognition-enabled Framework for Mixed Human-Robot Rescue Teams

Cited by 13 publications

References 16 publications

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification

Ontological concepts for information sharing in cloud robotics

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

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