基于概念地图的机器人导航方法

Conceptual maps based navigation for mobile robots

针对服务机器人提出一种环境概念地图模型,以在语义知识空间进行任务规划.该概念地图模型具有语义知识、空间知识及用户知识三层结构,能实现环境场景知识推理以及从概念规划到实例感知空间的映射.基于场景识别的概念地图创建与更新机制赋予了机器人自主环境学习能力.利用该概念地图模型,提出了一种在大规模室内环境下的拓扑与栅格分层导航策略,即通过地图切换技术维持在小尺度地图范围内进行Monte Carlo定位及栅格路径规划.在较大规模室内环境下的导航实验结果验证了机器人定位导航具有良好的性能和效率,实现了一种机器人的语义导航方式.

A conceptual map model of environments is proposed for task planning of service robots in semantic knowledge space. Such model is characterized with a layered structure containing ontology,spatial and user knowledge. It is capable of inferring environmental knowledge and mapping semantic plans onto the instantial perception space. A scene recognition based method for conceptual map building and updating is proposed to support the environment learning capabilities of robots. Using such model,a layered topological and metric navigation strategy is proposed,in which map switching technique ensures that Monte Carlo localization and metric path planning are performed within each small-scale grid map. Experimental results in large-scale office environments validate the effectiveness and efficiency of robot localization and navigation,and a semantic navigation manner is achieved.