In the face of unstructured home environment, home service robots are inevitably confronted with uncertainty and incompleteness of environment information. How to make the home service robot obtain enough environment information and plan a discrete sequence of actions through task planning is the key problem of robot intelligence. In this paper, a hierarchical task network based on semantic knowledge and probabilistic inference method is proposed. We use the object location ontology, the location relation between dynamic and static objects to build semantic knowledge of home environment, and build the probability model between dynamic and static objects, as well as between static objects and home scenes. The location of the object is determined by the semantic knowledge and the probability model. Hierarchical task network is selected as an engine of task planner, which can be provided with the location information to improve the autonomy and effectiveness of robot task planning. In order to prevent task execution failure and enhance the adaptability of robot to unstructured home environment, a mechanism of task execution diagnosis and replanning is designed. Experimental results in simulation and real home environment demonstrate that our method can effectively improve the performance of service robot task planning and generate better task execution sequence.

面对非结构化的家庭环境，家庭服务机器人不可避免地面临环境信息的不确定性和不完整性。如何使家庭服务机器人获得足够的环境信息并通过任务计划来计划一系列离散的动作是机器人智能的关键问题。本文提出了一种基于语义知识和概率推理方法的分层任务网络。我们使用对象位置本体，动态和静态对象之间的位置关系来构建家庭环境的语义知识，并建立动态和静态对象之间以及静态对象和家庭场景之间的概率模型。对象的位置由语义知识和概率模型确定。选择分层任务网络作为任务计划器的引擎，可以为其提供位置信息，以提高机器人任务计划的自主性和有效性。为了防止任务执行失败并增强机器人对非结构化家庭环境的适应性，设计了一种任务执行诊断和重新计划机制。仿真和真实家庭环境中的实验结果表明，该方法可以有效提高服务机器人任务计划的性能，并产生更好的任务执行顺序。设计了任务执行诊断和重新计划的机制。仿真和真实家庭环境中的实验结果表明，该方法可以有效提高服务机器人任务计划的性能，并产生更好的任务执行顺序。设计了任务执行诊断和重新计划的机制。仿真和真实家庭环境中的实验结果表明，该方法可以有效提高服务机器人任务计划的性能，并产生更好的任务执行顺序。