基于碰撞概率的交会对接近距离导引段的轨迹安全

提出了一种利用碰撞概率思想来描述交会对接近距离导引段飞行器轨迹安全的方法,这种方法采用最大瞬时碰撞概率来定量描述两个飞行器的碰撞危险程度。推导了最大瞬时碰撞概率的计算公式,其过程可分为三步:首先基于CW方程外推初始相对状态和误差协方差矩阵,然后利用外推后的相对状态和误差协方差矩阵计算瞬时碰撞概率,最后求出感兴趣区间内的最大瞬时碰撞概率。数值算例表明最大瞬时碰撞概率是描述交会对接近距离段两个飞行器碰撞危险程度的一个较好的指标。     

行为控制月球车的虚拟主体避障技术

论述了基于行为代理体的月球车避障技术。由月球地形高程图得到当地水平面下二色障碍图及着色障碍图，利用月球车代理体在障碍图及着色障碍图中进行避障学习与控制，将获得的控制量投影到月球车体坐标系下得到真实地形上的控制量，达到避障控制与运动规划的目的。利用障碍入侵月球车安全线的径向距离作为避障控制器输入，并由月球车目标趋向行为及避障行为输出融合突现出趋向目标的避障行为。仿真结果表明基于代理体的避障控制器设计方法使运行于真实环境下月球车的避障具有很高的可靠性。     

具有有界控制的三维最优制导律

在本文中以微分对策为基础，研究了导弹－目标飞行器在碰撞轨线附近的三维空间拦截问题。考虑了导弹、目标飞行器的动态特性，并将终端脱靶量作为性能指标，给出了完全信息情况下具有有界控制的双方最优策略。对时空区域进行了划分，讨论了导弹与目标相撞的条件以及导弹在实现最优制导律时存在的困难及实现方法。     

飞行器低空突防中的威胁航线优化技术研究

低空突防中的威胁回避技术是TF／TA^2系统的核心，直接关系到低空突防飞行器的任务生存能力。针对威胁回避的特殊要求，本文提出了一种基于地形可视性分析的威胁危险指标快速计算方法，解决了威胁的量化问题。针对在大范围任务区域内进行威胁航线优化存在计算复杂性和收敛性等问题，本文尝试采用遗传算法对威胁进行优化处理。该方法得到的威胁航线，严格经过出发点和目标点，且尽量远离威胁，有效提高了飞行器低空突防的任务生存率。     

TF／TA^2飞行控制系统设计—问题和方法

综合ＴＦ／ＴＡ＾２飞行控制系统的研究目的就是开发一种飞行控制技术,使飞机可以进行超低空机动飞行,有效地回避山峰,建筑物以及各种威胁,提高飞机的飞行安全和任务生存能力。本文介绍了综合地形跟随／地形回避／威胁回避飞行控制系统的系统框架和主要功能模块讨论在本文２设计过程中遇到的主要理论和技术问题,并阐述了了解决问题的思路和方法。     

RANDOM TERRAIN MODEL

在超低空突防技术研究过程中，需要对ＴＦ／ＴＡ算法进行测试。为了解决一个实时测试问题，本文在将地形定义为相关性是指数衰减的随机过程的基础上，给出了一个离散的两维一阶自递归算法产生随机地形数据。对不同的地形情况测试了ＴＦ／ＴＡ优化算法，该算法拥有可调的粗糙度参数。     

非合作目标自主交会对接的椭圆蔓叶线势函数制导

针对非合作式航天器自主交会对接任务的安全性要求,提出了一种基于椭圆蔓叶线的人工势函数制导方法。首先根据视线坐标系建立了相对动力学方程与状态方程。进而应用人工势函数制导方法解决了非合作目标航天器自主交会对接与静态障碍物躲避问题,并且把势函数方法与椭圆蔓叶线函数相结合,解决了追踪航天器在接近目标航天器时运行在安全走廊中的安全性要求。应用Lyapunov稳定性理论证明了在所提出的制导方法控制下系统的稳定性。最后,用精确的数学模型进行了计算机数值仿真,验证了所提出的制导控制方法的正确性和有效性。
     

Group force mobility model and its obstacle avoidance capability

Many mobility models attempt to provide realistic simulation to many real world scenarios. However, existing mobility models, such as RPGM [X. Hong, M. Gerla, G. Pei, C. Chiang, A group mobility model for ad hoc wireless networks, in: Proceedings of ACM/IEEE MSWiM’99, Seattle, WA, August 1999, pp. 53–60] and others, fail to address many aspects. These limitations range from mobile node (MN) collision avoidance, obstacle avoidance, and the interaction of MNs within a group. Our research, the group force mobility model (GFMM) [S.A. Williams, D. Huang, A group force mobility model, Appeared at 9th Communications and Networking Simulation Symposium, April 2006], proposes a novel idea which introduces the concept of attraction and repulsion forces to address many of these limitations. Williams and Huang [A group force mobility model, Appeared at 9th Communications and Networking Simulation Symposium, April 2006] described some of the limitations and drawbacks that many models neglect. This model effectively simulates the interaction of MNs within a group, the interaction of groups to one another, the coherency of a group, and the avoidance of collision with groups, nodes, and obstacles. This paper provides an overview of GFMM and particularly illustrates the GFMM's ability to avoid collision with obstacles, which is a vital property to posses in order to provide a realistic simulaition. We compare our model with the commonly used RPGM model and provide statistical assessments based on connectivity metrics such as link changed, link duration, and relative speed. All will be detailed and explained in this paper.     

Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach

In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.     

Three-dimensional path planning for unmanned aerial vehicle based on interfered fluid dynamical system

This paper proposes a method for planning the three-dimensional path for low-flying unmanned aerial vehicle(UAV) in complex terrain based on interfered fluid dynamical system(IFDS) and the theory of obstacle avoidance by the flowing stream. With no requirement of solutions to fluid equations under complex boundary conditions, the proposed method is suitable for situations with complex terrain and different shapes of obstacles. Firstly, by transforming the mountains, radar and anti-aircraft fire in complex terrain into cylindrical, conical, spherical, parallelepiped obstacles and their combinations, the 3D low-flying path planning problem is turned into solving streamlines for obstacle avoidance by fluid flow. Secondly, on the basis of a unified mathematical expression of typical obstacle shapes including sphere, cylinder, cone and parallelepiped, the modulation matrix for interfered fluid dynamical system is constructed and 3D streamlines around a single obstacle are obtained. Solutions to streamlines with multiple obstacles are then derived using weighted average of the velocity field. Thirdly, extra control force method and virtual obstacle method are proposed to deal with the stagnation point and the case of obstacles’ overlapping respectively. Finally, taking path length and flight height as sub-goals, genetic algorithm(GA) is used to obtain optimal 3D path under the maneuverability constraints of the UAV. Simulation results show that the environmental modeling is simple and the path is smooth and suitable for UAV. Theoretical proof is also presented to show that the proposed method has no effect on the characteristics of fluid avoiding obstacles.