In the last decades, the focus has shifted towards mobile robots to link predictions, imagination, and expectations to human life in different aspects. A tremendous amount of research on mobile robots indicates their importance in various industrial and non-industrial fields such as production, medicine and agriculture. Despite all of these innovations in the field of robotics, intelligent mobile robots are facing challenges in processing the vast amount of sensory data from their sensory inputs. Due to the increasing amount of sensory data, a new demand is to process sensory inputs in an understandable form for both humans and robots. One approach to processing sensory data in a way that is understandable for both robots and humans is through the use of semantic technology, which is a major technology for building semantic knowledge bases in a machine-readable form. The success of semantic technology is highly reliant on ontologies which are considered the semantic knowledge representation. The huge amount of research in this field proves the undeniable impact of ontologies in the field of robotics. Yet, the work concerning the conversion of the sensory inputs from a mobile robot into semantic information in real-time are scarce. This transformation becomes more challenging when converting the sensory input of multiple sensors to a single semantic statement. The collection of semantic information for real-time ontology population is another challenge. In this regard, there is also a lack of work in transferring and using this information for natural language communication between humans and robots. This work addresses these challenges and employs semantic technology in the field of robotics to enable a mobile robot to create semantic information during exploration. In addition, the resulted semantic information is used for communicating information as well as facilitating communication between other robots and humans in a natural language.

中文翻译：

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用于语义环境描述和人机交互的机器人语义协议（RoboSemProc）

在过去的几十年中，重点已转向移动机器人，以将预测，想象力和期望与人类生活的各个方面联系起来。对移动机器人的大量研究表明，它们在各种工业和非工业领域（例如生产，医药和农业）中的重要性。尽管在机器人技术领域进行了所有这些创新，但是智能移动机器人在处理来自其感官输入的大量感官数据时仍面临挑战。由于感官数据的数量不断增加，对人类和机器人而言，以可理解的形式处理感官输入的新需求是。以一种对机器人和人类都可以理解的方式来处理感觉数据的方法是使用语义技术，这是一种以机器可读形式构建语义知识库的主要技术。语义技术的成功高度依赖于被视为语义知识表示形式的本体。在这一领域的大量研究证明了本体论在机器人领域的不可否认的影响。然而，关于将来自移动机器人的感觉输入实时转换成语义信息的工作很少。当将多个传感器的感觉输入转换为单个语义陈述时，这种转换变得更具挑战性。实时本体的语义信息收集是另一个挑战。在这方面，在将这些信息传输和使用以在人与机器人之间进行自然语言通信方面也缺乏工作。这项工作解决了这些挑战，并在机器人技术领域采用了语义技术，以使移动机器人能够在探索过程中创建语义信息。另外，所得到的语义信息用于交流信息，并促进其他机器人与人类之间以自然语言进行交流。