陆相混积细粒储集岩岩相构型及其对甜点的控制作用

就陆相中高成熟度混积型页岩油开发而言,岩相对甜点的控制作用还不明确。以准噶尔盆地吉木萨尔凹陷二叠系芦草沟组为例,利用岩相构型分析、无人机倾斜摄影和三维地质建模等方法,结合岩心、露头、测井、分析化验和试油动态等资料,开展了岩相构型对甜点控制作用的研究。结果表明,把传统人工考察与无人机倾斜摄影技术结合起来,从井点设置、岩相参数定量刻画、格架模型和三维岩相原型模型建立等方面出发,可以表征芦草沟组露头剖面岩相构型三维空间分布特征。混积细粒储集岩岩相构型组合主要有渐变型、突变型和特殊型3类：渐变型砂体发育程度较高,形成上、下对称状岩相构型,常表现为多个相似旋回的叠置,烃源岩优质,储集层溶蚀孔隙发育,是优质甜点;突变型砂体发育程度一般,形成非对称状岩相构型,常呈多个相似旋回的叠置,烃源岩较为优质,储集层厚度薄;特殊型属于厚层碎屑岩类型,砂体发育程度高,呈单一旋回砂体叠置,储层较为致密,为相对优质甜点。储集岩岩相构型控制了甜点分布,并影响着开发部署策略。渐变型甜点发育程度高,非均质性弱,开发效果最好,采用大规模水平井平台化作业进行效益开发;突变型非均质性强,开发效果一般,是后续接替区域;特殊型整体呈分散状,连续性较差,非均质性强,需精细研究岩相构型空间分布,结合核磁共振法所测的物性参数和含油气性参数部署水平井开发。     

便携式地面测控系统

为适应无人机地面测控技术的发展要求,设计一种便携式无人机地面控制系统。分析便携式无人机控制系统的基本技术和原理,着重探究便携式地面测控系统的工作原理,通过采用操纵杆和键盘指令由测控计算机完成向无人机靶机发送遥控指令。试验和测试结果表明,所设计的无人机靶机地面测控系统具有一定的可行性。     

无人机在抢险救灾中的优化应用

针对在抢险救灾中的多无人机路径规划问题,文中采用遗传算法通过对无人机群的巡航矩阵进行编码组成染色体,设置不同个体数目和最大遗传代数优化巡航轨迹,同时采用0-1规划和非线性规划建立无人机终端流量传输矩阵,为每一个用户分配恰当的功率,使得无人机完成所有任务的时间总和最短。遗传算法分析结果:当从H 和J两处各派出3架无人机时至少需要11.37 h才能完成对7个重点区域的生命迹象搜索,此时巡航总距离为3 808 km,从H处派出3架无人机携带通信装备,并向灾区内的72个地面终端发送不同内容,至少需要25.81 h才能完成所有任务,此时巡航总距离为1 547 km。     

Cylindrical lithium-ion structural batteries for drones

The low cost, simplicity, and easy use of battery-powered multirotor aircraft has led to their adoption in commercial, industrial, agricultural, and military applications. These aircraft, however, have limited payloads and shorter endurance and range than fuel-powered conventional aircraft. To extend these key performance metrics, a structural battery is developed that uses commercially available battery cells as load bearing and power source elements for weight critical applications. The cylindrical structural battery is tested in three-point bending and is found to have four times higher stiffness and two times higher yield strength than the structure without battery reinforcement. Simulations of a quadcopter, redesigned with the proposed cylindrical structural batteries, demonstrate 41% longer hover time.     

Ground object recognition and segmentation from aerial image‐based 3D point cloud

Several attempts have been made to grasp three‐dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low‐level point information (coordinates and colors) and high‐level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo‐referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance‐based clustering and color‐based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.     

军民融合背景下无人机配送中心选址问题研究

结合“平时服务、急时应急、战时应战”的军民融合战略背景,考虑不同类型的需求点及配送中心,建立了一个以网络总里程最小为目标,以投资预算、规模效益、网络抗毁性等为约束条件的纯整数线性规划模型,据此对无人机配送中心的选址进行决策。算例分析验证了模型的可行性、有效性及大规模问题的适用性。参数分析表明在其他条件不变情况下,网络时效性与投资预算正相关,与规模效益、建设成本和网络抗毁性负相关,决策者需在网络时效性与投资预算、规模效益、建设成本、网络抗毁性之间进行权衡。     

Impact of motion blur on stereo‐digital image correlation with the focus on a drone‐carried stereo rig

Stereo‐digital image correlation (DIC) is a wide‐spread technique in the field of experimental mechanics for measuring shape, motion, and deformation and it is frequently used for material identification by using inverse methods (e.g., virtual fields method and finite element model updating). New applications emerge due to the reached maturity level of the technique, which poses new challenges towards reaching a desired level of accuracy in operating conditions. In this work, the possibility of a drone carrying an in‐house‐made portable DIC setup is explored, and the effect of the drone‐induced vibrations on the accuracy of stereo‐DIC for shape and strain measurement is evaluated. During acquisition, the relative motion between the camera system and the measured item generates motion‐blurred images. The effect of this phenomenon on the precision of stereo‐DIC is further evaluated in this paper.     

Visual model‐predictive localization for computationally efficient autonomous racing of a 72‐g drone

Drone racing is becoming a popular e‐sport all over the world, and beating the best human drone race pilots has quickly become a new major challenge for artificial intelligence and robotics. In this paper, we propose a novel sensor fusion method called visual model‐predictive localization (VML). Within a small time window, VML approximates the error between the model prediction position and the visual measurements as a linear function. Once the parameters of the function are estimated by the RANSAC algorithm, this error model can be used to compensate the prediction in the future. In this way, outliers can be handled efficiently and the vision delay can also be compensated efficiently. Theoretical analysis and simulation results show the clear advantage compared with Kalman filtering when dealing with the occasional large outliers and vision delays that occur in fast drone racing. Flight tests are performed on a tiny racing quadrotor named “Trashcan,” which was equipped with a Jevois smart camera for a total of 72 g. An average speed of 2 m/s is achieved while the maximum speed is 2.6 m/s. To the best of our knowledge, this flying platform is currently the smallest autonomous racing drone in the world, while still being one of the fastest autonomous racing drones.     

A variable neighborhood search for flying sidekick traveling salesman problem

The efficiency and dynamism of unmanned aerial vehicles, or drones, have presented substantial application opportunities in several industries in the last years. Notably, logistic companies have given close attention to these vehicles to reduce delivery time and operational cost. A variant of the traveling salesman problem (TSP), called the flying sidekick traveling salesman problem, was introduced involving drone‐assisted parcel delivery. The drone launches from the truck, proceeds to deliver parcels to a customer, and then is recovered by the truck at a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic where the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the general variable neighborhood search is employed to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve significantly delivery time. Furthermore, we provide a new set of instances based on the well‐known traveling salesman problem library instances.     

激光平面扫描3D测量系统快速标定技术

建立了激光平面扫描3D测量数学模型，提出了一种全新的3D测量系统参数的快速标定方法，设计了由2个完全垂直的平面组成的立体标定靶标，使用1个靶标可同时标定测量系统的摄像机参数和光平面方程参数。采用该方法对激光平面扫描测量系统进行了参数标定，用标定后的系统对标准平面进行了测量，空间测量精度优于0．1mm。