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Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

机译：基于类型2模糊本体的语义知识，用于自动驾驶水下航行器避免碰撞

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The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators' limitations, ontology-based knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user's query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protege OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs. (C) 2014 Elsevier Inc. All rights reserved.
机译：在海洋环境中遇到的障碍物数量正在迅速增加，这使得防撞系统的开发更具挑战性。已经提出了几种基于模糊本体的仿真器，以提供用于海上任务分析的虚拟平台。但是，由于模拟器的局限性，基于本体的知识不能用于评估海上机器人算法并避免冲突。现有的模拟器必须配备智能语义域知识，才能为AUV的决策系统提供有效的框架。本文为海洋用户提供了基于类型2模糊本体的语义知识（T2FOBSK）和一个模拟器，该模拟器将减少实验时间和减少海洋机器人的成本，并将对算法进行智能评估。系统重新格式化用户的查询，以提取AUV和障碍物的位置，并将其转换为适合模拟器的格式。模拟器使用语义知识来计算碰撞风险的程度并避免障碍。可用的基于类型1模糊本体的方法无法从朦胧的海洋环境中提取严重模糊的数据以提供实际的解决方案。因此，我们提出了一种2型模糊本体，以在实时海上作业期间提供有关碰撞风险和海洋环境的准确信息。此外，使用Protege OWL-2工具设计了2型模糊本体。 DL查询和SPARQL查询用于评估本体。到最接近点的距离（DCPA），到最接近点的时间（TCPA）和指南针度的变化（VCD）用于计算AUV与障碍物之间的碰撞风险程度。实验和仿真结果表明，所提出的架构对于海上任务和AUV的实时决策系统是高效和高产的。 （C）2014 Elsevier Inc.保留所有权利。

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