基于改进多元优化算法的动态路径规划

为满足动态路径规划实时性强和动态跟踪精度高的需求,提出一种基于能够同时发现并追踪多条最优以及次优路径的改进多元优化算法(IMOA)的求解方法。首先,通过利用贝赛尔曲线描述路径的方法把动态路径规划问题转化为动态优化问题;然后,把相似性检测操作引入到多元优化算法(MOA)中,增加算法同时跟踪多个不同最优以及次优解的概率;最后,用IMOA对贝赛尔曲线的控制点进行寻优。实验结果表明:当最优路径由于环境变化而变为非优或者不可行时,利用IMOA对多个最优以及次优解动态跟踪的特点,能够快速调整寻优策略对其他次优路径进行寻优以期望再次找到最优路径;其综合离线性能较其他方法也有一定的提高。因此,IMOA满足动态路径规划的实际需求,适用于解决动态环境中的路径规划问题。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

Effects of pressure oscillation on aerodynamic characteristics in an aero-engine combustor

The effects of pressure oscillation on aerodynamic characteristics in an aero-engine combustor are investigated. A combustor test rig is designed to simulate the pressure drop characteristics of a practical annular combustor. The pressure drop characteristics are firstly measured under atmosphere condition with non-reacting flow (or cold flow), and the air mass flow proportion of each component (dome/liner) are obtained; these properties are base lines for comparison with combustion state. The combustion tests are then carried out under conditions of inlet temperature 340–450 K, fuel air ratio 0.010–0.028. The stability map and the oscillation frequencies are obtained in the tests, the results show that pressure oscillation amplitude increases with the increase of fuel air ratio. Phase trajectory reconstruction is applied to classify the pressure oscillation motion; there are three motions captured in the tests including: “disk”, “ring” and “cluster”. The pressure drops across the dome under strong pressure oscillation are distinctly divergent from the cold flow, and the changes of pressure drops are mainly affected by pressure oscillation amplitude, but is less influenced by pressure oscillation motion nor oscillation frequencies. Based on the mass flow conservation, the reduction of effective flow area of combustor under strong pressure oscillation is demonstrated. Liner wall temperatures are analyzed through Multiple Linear Regression (MLR) method to estimate the reduction of the air mass flow proportion of the liner cooling under strong pressure oscillation. Finally, the air mass flow proportions of each component under strong pressure oscillation are estimated, the results show that the pressure oscillation motion also has influence on air mass flow proportion.     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.     

危险天气下基于多重Morphin算法的终端区三维实时改航方法

基于多重Morphin算法,建立了终端区 三维实时改航方法。该方法首先根据航空器当前飞行状态按不同的转弯角和爬升/下滑角生 成一组弧线,随后在每条弧线的末端按同样方式反复运行,形成若干条由弧线组成的路径, 最后对所有路径进行综合评估,找到当前时刻的改航路径。算例分析表明,该方法提供的改 航路径可以保证航空器运行的安全与高效,在危险天气出现时更加充分地利用终端区空域资 源,同时计算时间短、可行性高。     

飞船跳跃式再入第一次再入能量管理方法

针对探月飞船跳跃式再入第一次再入能量管理问题,设计了一种能量管理方法。首先,采用大倾侧角再入,快速进入能量管理阶段;然后,对能量耗散速度进行控制,并预估跃起点的能量以确定飞船的跃起时机;最后,在能量管理结束后按设计指令过渡到跃起点。为检验能量管理方法的有效性及鲁棒性,基于标准轨迹法设计制导律,并进行仿真分析。仿真结果表明,该能量管理方法能改善轨迹跟踪性能,提高再入制导方法的精度和鲁棒性,并避免飞船在一次再入和二次再入过程中出现较大过载的现象。     

Multiobjective genetic optimization of Earth–Moon trajectories in the restricted four-body problem

In this paper, optimal trajectories of a spacecraft traveling from Earth to Moon using impulsive maneuvers (ΔV maneuvers) are investigated. The total flight time and the summation of impulsive maneuvers ΔV are the objective functions to be minimized. The main celestial bodies influencing the motion of the spacecraft in this journey are Sun, Earth and Moon. Therefore, a three-dimensional restricted four-body problem (R4BP) model is utilized to represent the motion of the spacecraft in the gravitational field of these celestial bodies. The total ΔV of the maneuvers is minimized by eliminating the ΔV required for capturing the spacecraft by Moon. In this regard, only a mid-course impulsive maneuver is utilized for Moon ballistic capture. To achieve such trajectories, the optimization problem is parameterized with respect to the orbital elements of the ballistic capture orbits around Moon, the arrival date and a mid-course maneuver time. The equations of motion are solved backward in time with three impulsive maneuvers up to a specified low Earth parking orbit. The results show high potential and capability of this type of parameterization in finding several Pareto-optimal trajectories. Using the non-dominated sorting genetic algorithm with crowding distance sorting (NSGA-II) for the resulting multiobjective optimization problem, several trajectories are discovered. The resulting trajectories of the presented scheme permit alternative trade-off studies by designers incorporating higher level information and mission priorities.     

基于比例危险-比例优势模型的加速寿命试验设计

将基于信息的优化方法引入基于非参数模型的加速寿命试验优化设计中,针对恒定应力和步进应力两种应力加载方式,分别给出了基于比例危险-比例优势模型的试验优化设计方法.通过对数似然函数建立Fisher信息矩阵和方差-协方差矩阵,并采用基于信息的优化方法建立最优化问题.这种试验优化设计方法可以有效地提高模型参数评估精度,并且避免了传统的优化方法(即将一个与可靠性相关的函数的渐进方差在一个给定区间内的积分值作为优化目标)当目标函数中给定的积分区间变化时将得到不同优化结果的局限.最后给出了应用该方法进行加速寿命试验优化设计的仿真实例.     

基于对数螺旋线的非开普勒轨道设计

基于形状的方法为非开普勒轨道设计提供了一种全新的研究思路。在假定轨道形状为对数螺旋线的前提下设计了拦截轨道。首先通过无量纲化处理方法推导出了对数螺旋线轨道的地心距、极角随时间的变化率与轨道设计参数q的关系式;其次结合运动方程,得到了飞行器沿对数螺旋线轨道运行时需要施加的推力加速度;接着分别针对初始轨道是圆和椭圆的情况进行机动轨道设计。给出了轨道设计的仿真算例和相关分析,结果表明对数螺旋线适宜于拦截轨道设计;当初始轨道为大偏心率椭圆时,采用此方法设计轨道,在一定相角范围内开始机动,可使飞行器运行时间短,且燃料消耗少。     

基于Gauss伪谱法的临近空间飞行器上升段轨迹优化

综合考虑密度变化、声速变化、发动机推力变化及地球引力等因素对飞行轨迹的影响,研究了与实际飞行环境更加相符的临近空间飞行器燃料最优爬升轨迹。针对该问题在气动数据处理和优化求解上存在的困难,提出一种基于Gauss伪谱法(Gauss Pseudospectral Method,GPM)的求解策略。首先,依据气动数据特点,设计拟合模型对气动参数进行高精度拟合;其次,为避免间接法和传统直接法的缺点,将Gauss伪谱法和序贯二次规划(Sequential Quadratic Programming,SQP)相结合,对存在边值及加速度约束的轨迹优化问题进行求解,获得最优飞行轨迹。仿真结果表明,在更为真实的飞行环境下,利用GPM和SQP相结合的方法可在5.83s获得一条精度为10-4～10-6左右的飞行轨迹。