基于气体扩散模型的天然气泄漏场景下无人机自主飞行控制算法研究  

UAV autonomous flight control in natural gas leakage scenarios based on a gas diffusion model

作　　者：温凯[1] 王伟 谢宜峰 韩旭 WEN Kai;WANG Wei;XIE Yifeng;HAN Xu(Beijing Key Laboratory of Urban Oil and Gas Transmission and Distribution Technology,China University of Petroleum-Beijing,Beijing 102249,China;Shenzhen DJI Innovation Technology Co.,Ltd.,Shenzhen 518108,China)

机构地区：[1]中国石油大学(北京)城市油气输配技术北京市重点实验室,北京102249 [2]深圳市大疆创新科技有限公司,深圳518108

出　　处：《石油科学通报》2021年第4期614-625,共12页Petroleum Science Bulletin

基　　金：国家重点研发计划(项目编号2017YFC0805800)资助。

摘　　要：随着天然气在我国能源的比重中占比越来越大,天然气的配套设施也越来越多,因此对于应急设备的需求也越来越高。当发生天然气泄漏事故时,传统的检测方法不仅存在着安全隐患,而且检测结果也用于事后分析,并没有实时反馈现场结果的方案。针对于以上问题,本研究利用无人机的优势来弥补传统检测方法的安全性差的缺陷和时效性差的缺陷,再基于高斯烟羽气体扩散模型,结合已知参数,可以计算得到泄漏现场的实时浓度分布,最后利用自主飞行算法控制无人机沿现场危险浓度面进行飞行,完成浓度测绘或者其他现场测绘任务。研究还从多个影响因素角度来分析各个影响因素对于模型结果中气体浓度分布的影响,并进行了结果比较。另外还引入了随机误差来模拟真实情况下的气体扩散分布。本研究中无人机自主飞行算法部分提出了一种“边飞边算”的构想,该构想将气体扩散模型与自主飞行算法相结合,利用实时计算浓度分布数据来为无人机的飞行提供依据,实现无人机在飞行检测的同时,进行气体分布的更新和飞行路线的优化。气体扩散模型部分利用无人机实时测量的浓度点来更新浓度分布,高斯气体扩散模型计算的初始结果总是存在一定的系统误差,本研究利用实时浓度点来更新浓度分布可以得到最真实的浓度分布情况,这样无人机自主飞行算法才能根据最新浓度分布来进行飞行路线的规划,得到最有效的飞行路线,实现最有效的自主飞行。最后,本研究经过仿真测试,基于气体扩散模型的无人机自主飞行算法在仿真环境中能够实现沿危险浓度面飞行的任务,完成指定的危险浓度面测绘任务,对于现场事故指导有着一定的现实意义。As natural gas accounts for an increasing proportion of energy resources,there are more and more natural gas supporting facilities,so the demand for emergency equipment is also increasing.When a natural gas leakage accident occurs,the traditional detection methods not only have potential safety hazards,but the detection results are also used for post-mortem analysis,and there is no real-time feedback of on-site results.In response to the above important problems,drones were used to compensate for the poor safety and timeliness of traditional detection methods in this paper.Based on the Gaussian plume gas diffusion model,the real-time leakage site can be calculated by concentration distribution,and the autonomous flight algorithm was used to control the drone to fly along the on-site dangerous concentration surface to complete concentration surveying and mapping or other on-site surveying and mapping tasks.The research also analyzed the influence of each influencing factor on the gas concentration distribution in the model and compared the results.In addition,random errors were introduced to simulate the gas diffusion distribution under real conditions.In this research,the autonomous flight algorithm of UAVs puts forward a concept of"calculating while flying",which uses real-time calculation of the gas diffusion model to guide the flight of the UAVs.The gas distribution will be updated and the flight route will be optimized while the UAV is still in flight.The gas diffusion model part uses the concentration points measured in real time by drones to correct the error of initial results calculated by the Gaussian gas diffusion model.Finally,the UAV autonomous flight control proposed in this paper was verified in an experimental case.The UAV autonomous flight algorithm based on the gas diffusion model can realize the task of flying along the dangerous concentration surface,and complete the specified dangerous concentration surface surveying and mapping task,which is very significant for on-site accident guidance.

关 键 词：无人机 气体泄漏 高斯烟羽模型 路径规划 自主检测 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249.1