作　　者： (曲伟亭）; (孙首群）;

机构地区： 上海理工大学机械工程学院,上海200093

出　　处： 《软件导刊》 2017年第9期68-71,共4页

摘　　要： 针对油气管道专用检测机器人运动控制系统进行研究。在深入分析管道内机器人运动特性的基础上,利用基于模糊控制与神经网络控制的环境自学习网络方法对环境信息进行学习判断。利用模糊神经网络方法对管道机器人的运行轨迹进行自学习跟随,同时解决了管道机器人障碍物识别与规避的问题。仿真结果表明,该方法能够实现管道机器人的环境识别、路径跟随与障碍物识别等功能,符合管道检测的实用要求,具有较高的实际应用价值。 The purpose of this paper is studying at the special oil and gas pipeline inspection robot ~s motion control system, based on the analysis of the pipe robot movement characteristics to studying and judging environmental information, which from the fuzzy control and neural network of self-learning network environment. Using fuzzy neural network theory to track the running orbit of the pipeline robot, the obstacle recognition and avoidance problem of the pipeline is solved by the fuzzy neural network method. The simulation results show that the method can realize the environment recognition, path following and obstacle idea tification of pipeline robot. The method can meet the practical requirements of pipeline inspection and has high practical applica tion value.

关 键 词： 油气管道检测 机器人 模糊神经网络控制 避障