Research on Ontology Modeling of Uncertain Environment Cognition for Unmanned Maritime Systems

Yin, Lili; Gu, Hui; Zhang, Rubo; Xie, Wei; Zhao, Qi; Zhang, Yanan

doi:10.1109/cac.2018.8623413

Cited by 2 publications

(7 citation statements)

References 3 publications

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“…The semantic knowledge framework expresses the SR process in a graphical way. The search task ontology establishes a semantic relationship between the situation information acquisition, event detection, and event recognition module semantics framework through the AUV, as shown in Figure 3 [17,18].…”

Section: Situation Ontology Modelmentioning

confidence: 99%

“…Ontology is a method for modelling structured knowledge of information systems. The system knowledge of the entire SR system can be described in different levels by a single ontology knowledge representation [17].…”

Section: Situation Ontology Modelmentioning

confidence: 99%

“…The main frames used for SA are data fusion architecture [6], intelligent circulation [7], Joint Directors of Laboratories data fusion model [8], OODA (Observation, Orientation, Decesion, Action) loop [9], SA global assessment technique [1], Dasarathy model [10], Omnibus model [11], The extended OODA model [12], knowledge representation model [13], intelligent control framework [14,15] and model-based diagnostics [16]. Several knowledge representation approaches can be found in the literature implementing information transfer protocols for robotics [17]. The major challenge in SR is the reasoning method, which needs reasoning under uncertainty and communicating mission assessments to unmanned vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary to use a single form of knowledge representation to express the whole SR system, so that the decision‐making module can dynamically extract different levels and types of SR results in each stage. In this way, situation ontology model has natural hierarchy, structure and inclusiveness, which can reflect the sharing and reusing of knowledge and the interoperability of the system [17].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Yin

Chen

et al. 2021

CAAI Trans on Intel Tech

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The purpose of this study is to provide a situation reasoning module for autonomous underwater vehicle (AUV), to make decision-making agents know what is the current situation, what needs to be done in the present situation, and what is the risk of task present situation. This will enhance efficiency for AUV. Situation reasoning for AUV aims to communicate mission assessments to decision-making agents. Thus, in solving this problem, it is important to design a new situation reasoning module for AUV. This article is presenting the situation reasoning module for AUV, which includes uncertain event detection, uncertain event recognition and ontology model of the situation. The article concludes by demonstrating the benefits of the situation reasoning module in a real-world scenario. A situation reasoning simulation is demonstrated in an AUV while performing a complex mission. The approach was validated by marine experiments and simulating experiments for AUV. Results show the effectiveness of situation reasoning approach.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Situation Ontology Modelmentioning

confidence: 99%

Section: Situation Ontology Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Yin

Chen

et al. 2021

CAAI Trans on Intel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ontology, as a modeling tool that can describe knowledge models at the semantic and knowledge levels, provides a normative description of concepts, lays the foundation for knowledge sharing and reuse, improves interoperability within and between systems, and uncovers some implicit data relationships. To this end, this paper applies the ontology to the modeling process of MUSVs environmental information ontology to realize its functions of representation, query, storage and processing at the semantic and knowledge levels [ 14 ].…”

Section: Ontology Modeling Of Environmental Informationmentioning

confidence: 99%

Research on Cooperative Perception of MUSVs in Complex Ocean Conditions

Yin

Zhang

2021

Sensors

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Since the working environment of Multiple Unmanned Surface Vehicles (MUSVs) is accompanied by a large number of uncertainties and various hazards, in order to ensure the collision avoidance capability of MUSVs in complex marine environments, the perception of complex marine environments by MUSVs is the first problem that needs to be solved. A cooperative perception framework with uncertain event detection, cooperative collision avoidance pattern recognition and environmental ontology model is proposed to realize the cooperative perception process of MUSVs using ontology and Bayesian network theory. The cooperative perception approach was validated by simulating experiments. Results show the effectiveness of cooperative perception approach.

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Research on Ontology Modeling of Uncertain Environment Cognition for Unmanned Maritime Systems

Cited by 2 publications

References 3 publications

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Research on Cooperative Perception of MUSVs in Complex Ocean Conditions

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