基于A~*蚁群算法的无人机航路规划

针对低空密集复杂环境,在构建环境模型及无人机模型的基础上,提出了一种综合运用A~*算法与蚁群算法的智能航路规划方法。其中A~*算法用于全局航路规划,蚁群算法用于局部路径重规划,利用A~*算法的导向性克服蚁群算法收敛速度慢的缺点,能够使无人机快速到达目标点。仿真结果表明,A~*蚁群算法不仅可以全局引导蚁群算法快速收敛,使无人机快速飞向目标点,同时也可以在局部环境中规避障碍,保证无人机的飞行安全。     

基于导向强化Q学习的无人机路径规划

随着无人机的广泛应用,其飞行能耗和计算能力面临着瓶颈问题,因此无人机路径规划研究越来越重要。很多情况下,无人机并不能提前获得目标点的确切位置和环境信息,往往无法规划出一条有效的飞行路径。针对这一问题,提出了基于导向强化Q学习的无人机路径规划方法,该方法利用接收信号强度定义回报值,并通过Q学习算法不断优化路径;提出"导向强化"的原则,加快了学习算法的收敛速度。仿真结果表明,该方法能够实现无人机的自主导航和快速路径规划,与传统算法相比,大大减少了迭代次数,能够获得更短的规划路径。     

基于CPSO的UAV编队集结路径规划

针对多无人机编队集结路径规划问题,提出了具有合作机制的分布式协同粒子群(CPSO)算法。为了满足无人机运动学约束,采用曲率连续的PH曲线作为备选路径。基于协同进化思想提出CPSO算法,为每架无人机规划出一条满足机间协同约束的最优安全可飞行路径。仿真结果表明,规划得到的多条路径能够满足无人机运动学约束、安全性及无人机之间的协同性要求;相比于协同进化遗传算法,CPSO算法搜索成功率更高,稳定性更好。     

基于改进启发式蚁群算法的无人机自主航迹规划

无人机自主航迹规划是未来无人机作战使用的关键技术难题。针对传统航迹规划方法存在的求解效率不高、实时性较差、容易陷入局部最优等缺点,提出一种基于改进启发式蚁群算法的无人机航迹规划。算法前期使用Dijkstra 算法进行初始化航迹,引入启发式信息,提高搜索效率;采用Logistic 混沌映射初始化信息素,增加解的多样性,提高算法收敛速度;算法中、后期采用多航迹选择策略和模拟退火机制,提高全局搜索能力,避免因收敛速度过快,陷入局部最优解。对该算法进行仿真分析,结果表明：在存在威胁和障碍的复杂环境中,本文的改进蚁群算法与标准蚁群算法相比,能够有效规划出一条从起点到终点的航迹,并且寻优精度更高,收敛速度更快,具有一定应用价值。     

低空无人机路径规划算法综述

随着无人机技术的发展,无人机在低空的应用场景越来越多,复杂的低空环境对无人机路径规划算法提出了新的要求。本文总结了近年来常用的无人机路径规划算法,包括图搜索算法,线性规划算法,智能优化算法（遗传算法、粒子群算法、蚁群算法）,强化学习算法;对这些算法的原理、适用场景及其优缺点进行了归纳分析;并基于无人机发展现状对无人机路径规划算法进行了展望。     

基于Voronoi图和蚁群算法的无人机航迹规划

为提高无人机任务环境模拟的真实性,利用改进后的Voronoi图对任务环境进行建模。同时,为了更快地生成一条满足任务需求的最优飞行航迹,提高航迹规划的实战性和高效性,分析了蚁群航迹规划算法的运行原理,以及算法运行机制对算法性能的影响,提出了算法的改进原则,并在此基础上给出了新的信息素更新方式和新的启发式。利用改进后的蚁群算法,在改进型Voronoi图上进行了无人机航迹规划。计算机仿真结果表明,改进后的蚁群航迹规划算法与传统的蚁群航迹规划算法相比,运行时间更短,收敛速度更快,且得到最优航迹的概率更高,验证了算法改进原则的有效性。     

基于人工势场法优化的蚁群无人机航路规划

针对复杂环境下无人机航路规划问题,提出一种势场法优化的蚁群航路规划算法。为了改善蚁群初始路径搜索过程中的盲目性,将人工势场法的规划结果作为先验知识,对蚁群初始到达的栅格进行邻域信息素的初始化,进而运用改进的蚁群算法完成航路搜索任务。仿真结果表明,新算法具有收敛速度快,规划路径短以及环境自适应的优点。     

基于多指标动态优先级的无人机协同路径规划

针对复杂城市环境下多无人机（UAVS）协同巡检、配送等任务,提出一种基于多指标动态优先级的协同路径规划方法,以节省运行成本和增加任务效率。综合考虑碰撞风险、总路程、等待时间等指标构建动态优先级模型,并在优先级单边避碰机制下,定制组合规避策略以处理局部冲突,更好地权衡协同规划效率和路径质量。针对无人机个体路径规划,在Lazy Theta*算法基础上引入拥堵权值地图,引导无人机避开拥堵区域,降低冲突发生可能性。对比仿真试验表明：提出的个体规划算法可以减少拥堵区域和降低拥堵持续时间,提出的多指标动态优先级协同规划算法相比于飞行时间驱动的动态优先级,能够提高规划效率和结果最优性。     

基于改进蚁群算法的无人机三维航路规划

针对蚁群算法存在收敛速度慢,容易陷入局部最优而导致三维航路规划过程中出现规划时间过长、航路没有达到最优等问题,通过对蚁群算法进行改进,提出了一种天牛须融合改进蚁群的无人机航路规划优化算法,算法通过对蚁群算法的启发函数优化并进行蚁群择优排序,然后融合天牛须算法进行航路规划;将优化算法应用于无人机的三维航路规划中,使规划算法的运行速度更快,无人机的最优航路更短。同时用改进算法与天牛须、蚁群算法的收敛时间、最优路径长度进行对比。仿真实验结果表明,改进算法与另外两种算法相比,在算法收敛度、运行速度方面有明显的提升。     

基于改进蚁群算法的无人机航路规划

为了提高无人机(UAV)的作战效率和生存概率,在执行任务之前必须设计出高效的无人机飞行航路.针对这一问题,采用了蚁群算法进行航路规划,并对蚁群算法进行了改进.提出了保留最优解、自适应状态转换规则和自适应信息激素更新规则,有效的提高了算法算收敛速度和解的性能.最后用改进的蚁群算法对无人机任务航路进行了仿真,仿真结果表明,该算法是一种有效的航路优化算法.     

基于不规则障碍物环境下无人机的改进几何路径规划算法

随着无人机应用环境的多样化,在复杂环境中寻找无碰撞路径是非常重要的。传统的路径规划算法可以找到可行的路径,但它们在时间效率和路径长度之间没有很好的平衡,传统的几何算法只能避免特殊形状的障碍物。提出了一种改进的几何路径规划算法,使无人机能够在复杂的环境中避开任意形状的障碍物,找到较短的路径。首先,针对不规则障碍物,建立了凸多边形覆盖模型。然后解决了传统几何算法陷入局部最优解的缺点。提出了从相邻路径段生成无碰撞路径的二次规划思想,并针对该方法提出了一种新的安全阀值策略。最后,为了验证算法的性能,在不同的复杂环境下进行了仿真,并从几个方面对所提出的算法与A*算法进行了对比分析。     

基于改进ARA*算法的无人机在线航迹规划

针对无人机三维在线航迹规划对算法速率、航迹最优性的需求,提出了基于改进ARA^*算法的无人机在线航迹规划方法。首先,建立无人机三维航迹规划的数学模型;然后,提出了节点空间约简策略、局部启发项策略以提高算法收敛速率,并针对复杂规划环境提出了启发因子自适应递减策略。仿真结果表明,所提算法能够快速、稳定地生成首条可行航迹,并在剩余时间内不断提高航迹质量,可应用于不同类型的在线规划任务,动态地适应规划时间与航迹最优性的要求。     

基于流水避石原理的无人机三维航路规划方法

借鉴自然界流水避石现象,提出一种基于流体计算的无人机(UAV)三维(3D)航路规划方法.首先介绍了球心位于坐标原点时,球形障碍三维绕流问题的解析解.之后采用旋转平移矩阵与流线数据叠加方法生成了任意位置多障碍同时存在的三维流线.为验证解析解的有效性同时给出该方法基于数值模拟的计算过程,对适合无人机三维航路规划的流体模型和数值求解方法进行了分析,并给出了通过数值模拟求解航路的方法.最后,根据无人机机动约束对流线进行处理得到可飞航路,将航路长度、纵向和横侧向机动次数作为子目标函数对航路进行综合评价.仿真结果表明:解析法航路规划中,圆球障碍的地形建模简单计算量小,航路集中在由起点至终点的航路带间;数值法航路规划适合障碍分布复杂的地形,航路分布于规划空间中.这两种方法的航路平滑,能够满足无人机飞行约束,航路具有绕流意义的最优性,可以避免势场法的局部极小问题,并且可以提供多条备选航路.     

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles (UAVs) to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming (BLP) and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles (SAMs) are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.     

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.     

A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

In this paper, Active Disturbance Rejection Control(ADRC) is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV) to address the problem of height fluctuation during the transition from hover to level flight. Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller, a Multi-Strategy Pigeon-Inspired Optimization(MSPIO) algorithm is employed. Particle Swarm Optimization(PSO), Gen...     

Mission-oriented cooperative 3D path planning for modular solar-powered aircraft with energy optimization

Modular Solar-Powered Aircraft(M-SPA) is a kind of High-Altitude Long-Endurance(HALE) aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA. M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy. In this paper, firstly, oriented by the mission of maximizing the cruise area, the overall design of the M-SPA is modeled, including the energy model, the aerodynamic model and the...     

一种地理围栏内无人机航迹跟踪方法

针对UTM体制中无人机在地理围栏内的飞行监视问题,提出一种约束状态相关模态转换混合估计算法(CSDTHE)。采用随机线性混杂系统模型对无人机运动状态进行建模,利用CV、CT和CA三种模态描述无人机的飞行状态,以构建地理围栏内无人机运行的通用模态转换模型框架。利用飞行模态改变点(FMCP)定义相关模态转换参数,设计模态转换条件,生成模态转换概率矩阵,从而建立与状态相关的模态转换模型。运用约束卡尔曼滤波(CKF)方法对直线阶段和转弯阶段的无人机运动速度分别施加等式约束,并通过仿真实验验证了CSDTHE算法对无人机跟踪的有效性。     

Flight safety measurements of UAVs in congested airspace

Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles (UAVs) in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncer-tainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experi-ment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.