Elmogy Mohammed, Habel Christopher, Zhang Jianwei
Information Systems Department, Faculty of Computers and Information sciences, Mansoura University, Mansoura, Egypt.
Cogn Process. 2011 Feb;12(1):53-65. doi: 10.1007/s10339-010-0386-4. Epub 2011 Jan 4.
To build effective interactions between humans and robots, they should have common ground of understanding that creates realistic expectations and forms the basis communications. An emerging approach to doing this is to create cognitive models of human reasoning and behavior selection. We have developed a robot navigation system that uses both spatial language and graphical representation to describe route-based navigation tasks for a mobile robot. Our proposed route instruction language (RIL) is intended as a semi-formal language for instructing the robot to execute a route in an indoor environment. We implemented an instruction interpreter to process the route description and generate its equivalent symbolic and topological map representations. A topological map is generated to describe relationships among features of the environment in a more abstract form without any absolute reference system to treat the ambiguity which can occur when the robot cannot recognize the current landmark. The symbolic and topological map representations are supplied to other system components as an initial path estimation to guide the robot while it plans its navigation task. We conducted some experiments to evaluate the routes which are written by using the RIL instructions.
为了在人类与机器人之间建立有效的互动，它们应该有共同的理解基础，从而产生现实的期望并形成沟通的基础。一种实现这一目标的新兴方法是创建人类推理和行为选择的认知模型。我们开发了一种机器人导航系统，该系统使用空间语言和图形表示来描述移动机器人基于路线的导航任务。我们提出的路线指令语言（RIL）旨在作为一种半形式语言，用于指导机器人在室内环境中执行路线。我们实现了一个指令解释器来处理路线描述并生成其等效的符号和拓扑地图表示。生成拓扑地图以更抽象的形式描述环境特征之间的关系，而无需任何绝对参考系统来处理当机器人无法识别当前地标时可能出现的模糊性。符号和拓扑地图表示作为初始路径估计提供给其他系统组件，以在机器人规划其导航任务时为其提供指导。我们进行了一些实验来评估使用RIL指令编写的路线。
