基于集散式模型预测控制的多无人机协同分区搜索

针对多无人机在对大范围目标区域执行协同搜索任务时搜索资源分配不均、容易因频繁转场造成资源浪费等问题,借鉴集中式控制和分布式控制结构的优点,建立了集散式多无人机(unmanned aerial vehicle,UAV)协同搜索结构体系,通过聚类分析和V图划分等方法对目标区域进行分区,结合各子区域任务特点对无人机群进行搜索任务分配,并采用一种经改进后可有效增大UAV预测范围的预测控制模型,研究了动态环境下多UAV集散式协同分区搜索问题,最后,将所提方法与常见几种协同搜索方法进行对比仿真,获取仿真结果验证了所提方法在目标发现概率和搜索效率方面的有效性和优越性.     

针对飞行器的Muti-Agent仿真测试床的设计与实现

飞行器的Muti-Agent测试床是研究多无人机协同算法的重要平台。首先基于HLA标准完成了测试床构架的总体设计,然后通过测试床软件体系结构设计、运行流程设计、接口设计3个方面完成了整个系统的详细设计,最后通过使用VC++作为开发工具,采用地形LOD、MAPX控件、飞行器系统建模等方法实现了针对飞行器的Muti-A-gent分布式三维仿真测试床。仿真实例表明,该系统具有较强的通用性和逼真性。     

State transformation-based dynamic visual servoing for an unmanned aerial vehicle

In this paper, we propose a visual servoing control for a quadrotor unmanned aerial vehicle (UAV) which is based on a state transformation technique. The UAV is equipped with a single downwards facing camera, and the motion control objective is the regulation of relative displacement and yaw to a stationary visual target located on the ground. The state transformation is defined by a system of partial differential equations (PDEs) which eliminate roll and pitch rate dependence in the transformed image feature kinematics. A method for computing the general solutions of these PDEs is given, and we show a particular solution reduces to an established virtual camera approach. We treat point and line cases and introduce image moment features defined in the virtual camera image plane. Robustness of the control design is improved by accounting for attitude measurement bias, and uncertainty in thrust gain, mass, and image feature depth. The asymptotic stability of the closed-loop is proven. The method is based on a simple proportional-integral-derivative (PID) structure which can be readily implemented on-board. Experimental results show improved performance relative to previous work.     

无人机三维编队飞行的鲁棒H∞控制器设计

针对无人机编队控制中模型不确定性与外干扰同时存在的情况,首先基于无人机自身的自动驾驶仪和编队运动学关系建立了无入机编队的三维数学模型,这种建模方式物理意义明晰;进而提出一种基于鲁棒H∞控制理论的编队控制器设计方法,按前向、侧向和垂直方向3个通道分别设计控制律,降低了鲁棒控制器的调参难度,简化了三维编队控制问题.仿真结果表明了所设计的控制器的有效性,可实现无碰撞、快速、稳定地保持和调整无人机编队队形,具有良好的鲁棒性能.     

Coverage path planning for UAVs based on enhanced exact cellular decomposition method

In this paper, an enhanced exact cellular decomposition method to plan the coverage path of UAVs in a polygon area is proposed. To be more specific, the contributions of the paper are: firstly, the turning motion of UAVs is shown to be less efficient from the viewpoints of route length, duration and energy. Secondly, the problem of coverage Path Planning (CPP) in a convex polygon area is transformed to width calculation of the convex polygon, and a novel algorithm to calculate the widths of convex polygons with time complexity of O(n) is developed. The path of the least number of turns for an UAV based on the widths of convex polygons is devised. Thirdly, a convex decomposition algorithm for minimum width sum based on the greedy recursive method which revolves around decomposing the concave area into convex subregions is developed. It is proved that the algorithm is a polynomial time algorithm. To avoid unnecessary back and forth motion, some entirely adjacent subregions are combined. Finally, comparing different weights of two joint-points, a subregion connection algorithm based on minimum traversal of weighted undirected graph is proposed to connect the coverage paths of the subregions. Simulation results show that the proposed method is feasible and effective.     

车载多架无人机快速供电测试和启动系统设计

介绍一种车载多架无人机快速供电测试和启动系统的设计,包括车载地面检测计算机、直流电源、电气控制装置、发射箱供电测试及启动装置、地面检测计算机、供电测试接口装置和车载配电网络。其中车载直流电源为无人机供电测试和发动机启动提供电源;地面检测计算机发出的供电测试、启动指令送给电气控制装置,通过供电测试接口装置、车载配电网络向无人机供电,当无人机测试正常后,则可进行发动机启动。发动机的启动电源采用多个发射箱启动直流母线并联供电。该系统提高了多架无人机供电测试和启动发动机的自动化程度。     

基于遗传算法的飞行器路径规划研究

提出一种基于遗传算法和B-样条曲线的路径规划新方法。以B-样条曲线控制点的坐标作为遗传因子用遗传算法进行规划,根据这些控制点,运用B-样条曲线在3维坐标系中生成飞行器路径轨迹。由于只对控制点进行规划减小了系统的计算量,极大地提高了系统的实时性。仿真表明运用该算法规划的路径可以以最优的轨迹把飞行器指引到预定的目的地。     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

基于多策略融合粒子群的无人机对地攻击模糊博弈决策

针对无人机协同对地攻击的复杂性和不确定性,联合防空火力压制与对地目标打击任务,引入存活因子、摩擦因子和状态因子等概念,考虑目标威胁度的模糊性,结合生存概率和武器消耗等因素,建立一种多阶段的模糊多目标任务分配规划模型.为更好地描述攻击任务的对抗性和多策略性,以博弈论为框架,将规划模型转化为模糊多目标双矩阵博弈综合集结模型.利用必要性理论将集结模型中的不确定性目标清晰化处理,进而运用熵权法对多个目标进行加权求和,将其转化为单目标双矩阵博弈模型.提出基于多策略融合粒子群算法的纳什均衡求解方法,通过引入自适应惯性权重、动态反向学习与局部变异策略,在增强种群多样性的同时,保证粒子群局部精确搜索能力.算例仿真结果验证了所提模型和方法的有效性.     

Efficient data management and control over WSNs using SDN‐enabled aerial networks

The recent developments in collaborative search, acquisition, and tracking have hoisted the geographical barrier. The network between unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) is one such collaboration, which comprises battery‐powered static sensor nodes that act as sources and sinks and UAVs that act as relays. This collaborative network presents with opportunities and advantages, but at the same time, configuration of such networks is an arduous task. The WSN nodes are characterized by constant depleting power. Their network itself requires constant management and reconfiguration. These requisites can be slaked through the formation of an efficient data dissemination algorithm, which acclimates according to the network state. Considering this, a data dissemination approach is presented in this paper, which constructs a virtual topology predicated on the charge of WSN nodes utilizing software‐defined networks (SDNs) through UAVs. The topology is constantly monitored and reconfigured when required. The aerial nodes are equipped with multiple‐input multiple‐output (MIMO) antennas in order to facilitate simultaneous communication with the ground nodes, the base station, and the SDN controller. An efficient sleep timer and backoff counter strategies are also utilized by the proposed approach. The SDN controller facilitates the topology formation and maintenance of a sleep timer and a backoff counter. The proposed model is compared with clustered hierarchical layouts and hexagonal cell layouts through the network simulations. The results suggest significant improvements in the proposed model for various metrics, such as lifetime, delay, latency, delivery ratio, and throughput in comparison with the existing solutions.