AUV的图优化海底地形同步定位与建图方法

海底地形辅助导航技术可为智能水下机器人(AUV)的长时间水下作业提供有界精确导航结果,且无需声学基阵辅助。然而,地形辅助导航需要已知高精度先验海图,这大大限制了其应用范围。为解决海底地形未知条件下的海底地形辅助导航问题,提出了海底地形同步定位与建图技术。利用水深预测手段和地形匹配方法进行帧间数据关联构建和闭环检测,并通过图优化技术进行海底地形同步定位与建图问题求解。仿真试验结果显示,在航行1h后,海底地形同步定位与建图系统的建图结果中所有测深点定位误差均值约为13m。     

Dynamic airspace configuration method based on a weighted graph model

This paper proposes a new method for dynamic airspace configuration based on a weighted graph model. The method begins with the construction of an undirected graph for the given airspace, where the vertices represent those key points such as airports, waypoints, and the edges represent those air routes. Those vertices are used as the sites of Voronoi diagram, which divides the airspace into units called as cells. Then, aircraft counts of both each cell and of each air-route are computed. Thus, by assigning both the vertices and the edges with those aircraft counts, a weighted graph model comes into being. Accordingly the airspace configuration problem is described as a weighted graph partitioning problem. Then, the problem is solved by a graph partitioning algorithm, which is a mixture of general weighted graph cuts algorithm, an optimal dynamic load balancing algorithm and a heuristic algorithm. After the cuts algorithm partitions the model into sub-graphs, the load balancing algorithm together with the heuristic algorithm transfers aircraft counts to balance workload among sub-graphs. Lastly, airspace configuration is completed by determining the sector boundaries. The simulation result shows that the designed sectors satisfy not only workload balancing condition, but also the constraints such as convexity, connectivity, as well as minimum distance constraint.     

一种基于卫星节点的时变拓扑网络模型

卫星网络与地面固定网络有着本质的差别,特别是它的时变特性,使得成熟的固定拓扑网络理论不能很好地应用到卫星网络中。将卫星网络抽象为以时间为变量的周期函数矩阵,并引入图论,提出一种基于卫星节点的时变拓扑网络模型,讨论模型中节点间的关系和模型离散化条件,给出该模型的离散化形式以及离散时间序列选取原则和相应的证明和仿真,最后指出现有的虚拟节点、有限状态自动机、快照系列、动态虚拟拓扑等研究卫星网络的方法都可以纳入所提出的模型框架中。     

机械设计标准件图形库的建立

在使用AutoCAD绘图软件时,常常因其缺乏完善的标准件图形库而受到限制,为了解决这个问题,应用AutoLISP语言建立了机械制图中的标准件图形库。程序采用模块化结构,由菜单或命令驱动,在使用方法上与AutoCAD一致。     

基于隐式位姿空间分层量化的空间机械手无碰撞路径规划方法

本文在概述空间机械手无碰撞路径规划问题的特殊性的基础上提出了相应的路径规划策略。依据这些策略,本文描述了一种基于隐式位姿空间分层量化的路径规划划方法,并通过仿真验证了所提出规划策略和规划方法的有效性。     

Modelling and simulation of flight control electromechanical actuators with special focus on model architecting,multidisciplinary effects and power flows

In the aerospace field, electromechanical actuators are increasingly being implemented in place of conventional hydraulic actuators. For safety-critical embedded actuation applications like flight controls, the use of electromechanical actuators introduces specific issues related to thermal balance, reflected inertia, parasitic motion due to compliance and response to failure. Unfortu-nately, the physical effects governing the actuator behaviour are multidisciplinary, coupled and nonlinear. Although numerous multi-domain and system-level simulation packages are now avail-able on the market, these effects are rarely addressed as a whole because of a lack of scientific approaches for model architecting, multi-purpose incremental modelling and judicious model implementation. In this publication, virtual prototyping of electromechanical actuators is addressed using the Bond-Graph formalism. New approaches are proposed to enable incremental modelling, thermal balance analysis, response to free-run or jamming faults, impact of compliance on parasitic motion, and influence of temperature. A special focus is placed on friction and compliance of the mechanical transmission with fault injection and temperature dependence. Aileron actuation is used to highlight the proposals for control design, energy consumption and thermal analysis, power net-work pollution analysis and fault response.     

空间推进系统静态特性通用仿真方法

针对采用液体推进剂的空间推进系统,提出了一种基于图论和面向对象技术的通用数值仿真方法,该方法适用于空间推进系统各种形式设计方案的检验及推进系统静态工作过程的仿真研究。应用此方法开发的软件对某空间推进系统进行了静态特性的仿真,获得良好效果,显示了很好的通用性。     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.