Intelligent and Autonomous Flight Technology for Launch Vehicles

This paper proposes the architecture of an intelligent flight launcher system as well as fundamental solutions to capability prediction and dynamic planning. This effort reflects the latest progress in the applications of intelligent and autonomous technology for launcher flights. The paper first describes the characteristics and capabilities of intelligent and autonomous systems and classifies various related technologies. In the context of intelligent and autonomous technology in aerospace engineering, it then focuses on technical difficulties involved with intelligent flight and reviews developments in the field. An E^3 classification model of an intelligent flight launcher is then proposed and its application scenarios are discussed. Based on an intelligent flight system configuration of the launcher, the online trajectory planning and initial value guess are examined, and vertical landing is provided as an example to explain the effects of the implementation of computational intelligence to flight systems.

我国植保无人飞机机型发展概况

植保无人飞机具有快速高效、适应性广等特征,克服了传统地面植保机械作业效率低、下地难、劳动力投入大的问题,近年来发展迅速。本文系统叙述了植保无人飞机的发展及应用现状,介绍了日本与中国植保无人飞机的机型演变,从机体平台技术、附属作业部件技术、标准化与智能化作业技术4个方面介绍了我国植保无人飞机的技术发展过程,从飞行控制、喷雾性能与药箱电池3个方面对比了我国和日本植保无人飞机的产品性能,并对我国植保无人飞机的未来发展提出建议。

卫星巡天扫描运控模式方法的研究

巡天扫描是天文卫星进行天体观测的基础,但是使用传统方法很难实现全天球覆盖,并且会遇到能源输出瓶颈。因此,针对天文卫星巡天扫描所遇到的问题,提出了使用Pareto遗传算法对卫星飞行任务进行智能规划的方法。该算法引入小生境方法来保证群体多样性,并使用基于排序的适应度分配方法来克服早熟收敛。该算法智能规划飞行任务,与仅依靠被动的分段式扫描方法相比,在扫描全天球的基础上,很好解决了能源输出的问题,还大大缩短了巡天扫描的时间。从仿真结果来看,该模型是十分有效的。

基于实值编码GA的卫星巡天扫描方法

巡天扫描是天文卫星进行天体观测的基础,但是使用传统方法很难实现全天球覆盖,并且会遇到能源输出瓶颈。因此,本文针对天文卫星巡天扫描所遇到的问题,提出了使用基于实值编码的遗产算法(Real-codedGA)对卫星飞行任务进行智能规划的方法。该算法智能规划飞行任务,与仅依靠被动的分段式扫描方法相比,很好解决了能源输出的问题,还大大缩短了巡天扫描的时间。从仿真结果来看,该模型是十分有效的。

Reinforcement learning based UAV formation control in GPS-denied environment

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another UAV.In particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a UAV.In this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller.First,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight.In our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module.The k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information.Second,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry.Third,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group.Finally,a novel experience replay method is also provided in this paper to enhance the learning efficiency.Experimental results validate the accuracy,effectiveness,and robustness of the proposed scheme.