Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

基于蚁群算法的TSP的仿真与研究

对于旅行商问题的研究始于19世纪,从20世纪中叶开始随着计算机技术的发展而不断发展。蚁群算法是一种新型的优化算法,于20世纪90年代提出,最早成功应用于解决旅行商问题。研究表明,蚁群算法有着极强的鲁棒性发现较好解的能力。通过编程实现了用蚁群算法解决旅行商问题,通过仿真实验研究了各参数对算法的影响。     

终端区飞机排序的规划模型和算法研究

对终端区到达飞机进行排序是终端区交通管理自动化系统的一项主要任务。提出了一种新的终端区规划排序模型，把飞机的排序问题看作等价于带有准备好时间的渐增周游店员问题，并给出了兼顾解的最优性和计算复杂性的快速启发式算法。分析了静态和动态两种情况，并且考虑了实际的运行限制。计算结果表明，所给模型和算法具有良好的规划性能，能较好地解决终端区排序问题，可应用于我国空中战术流量管制系统。     

Modeling novel methodologies for unmanned aerial systems–Applications to the UAS-S4 Ehecatl and the UAS-S45 Bálaam

The rising demand for Unmanned Aerial Systems(UASs) to perform tasks in hostile environments has emphasized the need for their simulation models for the preliminary evaluations of their missions. The efficiency of the UAS model is directly related to its capacity to estimate its flight dynamics with minimum computational resources. The literature describes several techniques to estimate accurate aircraft flight dynamics. Most of them are based on system identification. This paper presents an alternative methodology to obtain complete model of the S4 and S45 unmanned aerial systems. The UAS-S4 and the UAS-S45 models were divided into four sub-models, each corresponding to a specific discipline: aerodynamics, propulsion, mass and inertia, and actuator. The‘‘aerodynamic\" sub-model was built using the Fderivatives in-house code, which is an improvement of the classical DATCOM procedure. The ‘‘propulsion\" sub-model was obtained by coupling a two-stroke engine model based on the ideal Otto cycle and a Blade Element Theory(BET) analysis of the propeller. The ‘‘mass and the inertia\" sub-model was designed utilizing the Raymer and DATCOM methodologies. A sub-model of an actuator using servomotor characteristics was employed to complete the model. The total model was then checked by validation of each submodel with numerical and experimental data. The results indicate that the obtained model was accurate and could be used to design a flight simulator.     

多无人机协同搜索区域划分与路径规划

针对多无人机广域协同搜索问题,研究无人机工作区间划分和全区域覆盖搜索路径规划2个子问题。采用按无人机来向均衡划分的方法和凹点凸分解的方法,开展了凸多边形和非凸多边形的区域划分研究,将多机协同搜索问题转化为子区域上的单机搜索问题;在此基础上采用\"Z\"型路径覆盖方法以及Dubins转弯路径,对各个无人机开展覆盖其子区域的搜索路径规划,从而建立了一个区域划分和路径规划的整体调用框架,能够对目标区域快速进行划分并生成飞行路线。最后,对凸多边形和非凸多边形区域搜索开展仿真计算,验证了该方法的有效性。     

基于混合遗传算法的中国旅行商问题满意解

针对组合优化中的旅行商问题 ,提出了贪心法与遗传算法相结合的混合遗传算法。文中给出了相应步骤并就中国旅行商问题得出满意解 ,该解优于已报道的结果。     

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.     

城市无人机航迹算法研究及仿真分析

近几年,无人机运输逐渐被广泛应用,无人机在城市实际运输航迹的规划急需解决。通过通用墨卡托投影法,将成都市特定区域内无人机实际投放的节点地理位置投影到二维坐标系下,利用Global Mapper计算并转换无人机二维运输航迹节点坐标,结合旅行商问题为无人机作业规划一条最短航迹,通过蚁群算法实现问题的最优化,运用MATLAB仿真并计算得到成都市特定区域无人机运输最短航迹网络。仿真结果验证了方法的正确性,为无人机城市运输规划最优航迹提供参考。     

Cooperative task assignment of multi-UAV system

With the rapid development of Unmanned Aerial Vehicle(UAV) technology, one of the emerging fields is to utilize multi-UAV as a team under autonomous control in a complex environment. Among the challenges in fully achieving autonomous control, Cooperative task assignment stands out as the key function. In this paper, we analyze the importance and difficulties of multiUAV cooperative task assignment in characterizing scenarios and obtaining high-quality solutions.Furthermore, we present three promising directions for future research: Cooperative task assignment in a dynamic complex environment, in an unmanned-manned aircraft system and in a UAV swarm. Our goal is to provide a brief review of multi-UAV cooperative task assignment for readers to further explore.     

无人机空中加油过程中基于机器视觉的相对位姿估计(英文)

本文主要研究了在无人机空中自主加油过程中,利用安装在无人机上的摄像机得到的视觉图像确定无人机与加油机之间相对位置关系的算法。本文以经典的OI算法为基础展开研究,文章主要分为三个部分:首先简要介绍了OI算法的基本实现方法,指出了在OI算法中,旋转矩阵R的初值的选择对于位姿估计的精度有一定的影响。其次主要介绍了一种线性算法用于获取R初值的方法,同时针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,提出了可以将旋转矩阵R的初值设为单位矩阵。论文最后在仿真环境下对相关算法展开实验研究,同时研究了在高斯噪声环境下特征点在加油机表面的分布对位姿估计结果的影响,论文得到如下的结论:1、针对空中加油过程中无人机与加油机之间的相对姿态角变化较小的特点,在高斯噪声环境下,以单位矩阵作为旋转矩阵的初值得到的位姿估计的结果好于以线性算法为初值的结果。2、在相同的高斯噪声环境下,特征点之间的距离越大时得到的位姿估计结果的精度越高。3、特征点的数量对相对位姿估计的精度没有明显影响。     

基于鲁棒H∞控制的多机协同编队

针对环境风干扰情况下无人机编队保持精度差的问题,设计了基于邻居无人机相对状态的编队控制协议。通过定义恰当的被控输出来量化环境风干扰对多机编队的影响,将受扰多机编队控制问题转化为鲁棒H∞控制问题。基于H∞控制方法,得到了满足期望H∞干扰抑制指标的多机编队充分条件,并以线性矩阵不等式的形式给出。此外,对于僚机之间的通信拓扑为无向图的情形,可以通过只求解2个线性矩阵不等式确定控制协议。最后数值仿真结果表明,该控制协议能够有效抑制阵风干扰对多机编队的影响,提高了多机协同编队的鲁棒性。     

低空空域无人系统交通管理方案初探

近年来;随着无人机（UAV）技术的飞速发展;全球各国开始积极布局低空空域管理;以确保低空经济的可持续发展。现有空中交通管理（ATM）系统主要面向中高空空域的有人驾驶飞机;难以适应大规模低空无人机的运行需求。尤其在物流配送、城市监控、基础设施巡检等领域;低空无人机的高密度运行对交通管理提出了新的挑战。因此;如何构建一套高效、安全、智能的无人系统交通管理（UTM）框架;成为全球研究的热点问题。首先;综述了国内外低空空域UTM系统的研究现状;重点分析了不同国家在空域划分、UTM系统建设以及政策实施等方面的异同。其次;给出UTM系统的定义和基本架构;着重梳理了多无人机通信网络、多无人机协同控制、高效容量流量管理、多无人机冲突管控和交通群自主运行等UTM系统的共性关键技术;并深入分析了当前技术发展面临的主要挑战。最后;基于分布式决策、实时空域协商及风险弹性管理的设计思路;结合中国低空空域管理的实际需求和中国北斗卫星系统（BDS）的技术优势;提出了一种基于“分层、分类、分域、网格化”管理理念的分阶段、分步骤实施的UTM解决方案;并创新性地设计了以“八卦图”为核心的“管道化”立体航线网络;以优化空域资源分配;支持未来大规模无人机自主协同运行。     

微小型无人机三轴磁强计现场误差校正方法

详细分析微小型无人机导航用三轴磁强计的误差来源,建立三轴磁强计的等效误差模型,提出基于两步估计算法和圆约束非对准误差估计算法的三轴磁强计现场误差校正方法.在充分考虑地磁场偏转和倾斜特性的基础上,提出适合微小型无人机应用的现场数据采样策略,能够在较少的旋转操作下获得较好的采样数据.仿真表明:在所有磁场误差都存在的情况下,...     

蚁群算法在宽弦风扇叶片优化排序中的应用

从风扇转子的力学模型出发,推导了同时考虑三个维度质量矩的风扇转子静不平衡量和偶不平衡量的计算公式,并使用某型发动机风扇叶片装配和试车验证,结果表明考虑三个维度质量矩的不平衡量计算方法能显著减少配重块的使用,并减少配平次数.提出了基于邻域搜索改进的蚁群算法的风扇叶片排序优化方法,对某套风扇叶片进行优化排序.本优化算法的最...