Department of Computer Science, Kyonggi University, San 94-6, Yiui-dong, Youngtong-gu, Suwon-si 443-760, Korea.
Sensors (Basel). 2018 Oct 5;18(10):3336. doi: 10.3390/s18103336.
Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.
服务机器人在室内环境中运行时，应能识别来自传感器（如 RGB-D 相机）的动态变化，并回忆过去的上下文。因此，我们为服务机器人提出了一个上下文查询处理框架，包括时空机器人上下文查询语言（ST-RCQL）和时空机器人上下文查询处理系统（ST-RCQP）。我们基于时空上下文本体对它们进行了设计。ST-RCQL 不仅可以查询当前上下文知识，还可以查询过去的知识。此外，ST-RCQL 包含各种时间运算符和时间常量；因此，可以非常高效地编写查询。ST-RCQP 是一个配备了感知处理器、工作内存和反向推理器的查询处理系统，用于实时查询处理。此外，ST-RCQP 通过在工作内存中构建一个存储感知的时空索引来加速查询处理速度。通过各种定性和定量实验，我们证明了所提出的上下文查询处理框架的高效性和性能。
