【作者】 张敬伟；

【导师】 柳长安；

【作者基本信息】 华北电力大学 ， 计算机应用技术， 2014， 硕士

【摘要】 核电站检修机器人作为核电站作业中减少工作人员核辐射剂量的重要工具,在核电站日常检修和事故抢修等过程中发挥着重要的作用。然而,核电站检修机器人是如何处理并执行核电站检修机器人控制中心下达给它的检修任务的?这一问题即为检修任务的分解与优化问题。任务分解与优化问题作为核电站检修机器人的核心问题之一,是控制核电站检修机器人完成检修任务的关键。因此,需要研究核电站检修机器人的任务分解以及分解完成之后的基元动作的执行方法。首先对基元动作的表示方法与执行方法进行了分析研究,针对核电站操作对象的特殊性,提出一种各个基元动作采取不同定义与表示的方法,然后利用有约束的随机生成方法和数据库匹配算法,提出基元动作的学习算法流程。该算法是自学习的,具有广泛的适用性和扩展性。设计并进行了该学习算法的仿真实验,通过分析仿真实验的结果验证提出算法的正确性与可行性。其次,针对检修任务分解与优化这一问题,通过结合核电站检修任务的背景知识,利用承担特质理论分析研究出一种适合于核电站检修问题的任务分解方法。该方法能够实现与基元动作的结合,实现核电站任务分解成基元动作并进行基元动作执行的过程,以完成课题的研究目标。再次,将贝叶斯网络模型引入到检修任务的分解问题中,构建出与承担特质理论相结合的检修任务分解模型。最后,通过设计并完成对该模型的仿真实验,详细的分析了该模型的应用步骤,以及该模型与基元动作的结构关系。通过分析实验结果,证明了该模型的有效性,以及对于动作正确率的提升。更多还原

【Abstract】 As an important tool to reduce staff radiation, maintenance robots for nuclear power plant play a significant role in the nuclear power plant accident repairs and routine maintenance and other processes. However, how can a nuclear power plant maintenance robot handle and perform its maintenance tasks to maintenance of nuclear power plants from robot control center? That is the problem of task decomposition and optimization. Task decomposition and optimization is one of the core issues of maintenance robots for nuclear power plants, and is the key to control nuclear power plant to complete maintenance tasks. Therefore, it is need to study the method of nuclear power plant maintenance robots to perform the task decomposition and execute primitive actions after decomposition.Firstly, the method of representation and execution of primitive actions are analyzed in this paper. With a view to particularity of nuclear power plant operation objects, we propose a method for each primitive action taking different definitions and representation, and then using a constrained random generation method and database matching algorithm, the learning algorithm processes for the primitive actions is proposed. The algorithm is self-learning, and has broad applicability and extensibility. By performing the simulation of the learning algorithm and analyzing the simulation results, the correctness and feasibility of proposed algorithms is verified. Secondly, we combine the background knowledge of nuclear power plants maintenance tasks, and use affordance theory to analysis and develop a suitable maintenance problems with nuclear power plant task decomposition methods. This method can be achieved with a combination with primitive actions to achieve nuclear power plant task into the process of primitive actions and to complete the research objectives topics. Thirdly, the Bayesian network model is introduced to decompose the problem of maintenance tasks to build a combining maintenance task decomposition model. Finally, through the design and complete simulation of the model, a detailed analysis of the application of the model, as well as the relationship between the model and the structure of primitive actions are demonstrated in this paper. By analyzing the experimental results, we obtain that the model could make a revise for human factor correction in priori Bayesian network model, and promote the accuracy for actions.更多还原