无人机的自动着陆控制

无人机的自动着陆控制是无人机控制中的一个难点,文中给出了一种无人机自动着陆的控制方法,并在某型无人机上得到了应用.基于无人机真实的风洞试验数据,利用MATLAB建立了无人机自动着陆的线性化模型和非线性模型,设计了无人机自动着陆导引控制方案及控制律,并在实际飞行试验中得到了验证,最终的飞行试验结果表明了所设计的自动着陆控制律能满足自动着陆的要求,并具有很好的静态和动态特性.     

Autonomous landing on a moving vehicle with an unmanned aerial vehicle

This paper addresses the perception, control, and trajectory planning for an aerial platform to identify and land on a moving car at 15 km/hr. The hexacopter unmanned aerial vehicle (UAV), equipped with onboard sensors and a computer, detects the car using a monocular camera and predicts the car future movement using a nonlinear motion model. While following the car, the UAV lands on its roof, and it attaches itself using magnetic legs. The proposed system is fully autonomous from takeoff to landing. Numerous field tests were conducted throughout the year‐long development and preparations for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 competition, for which the system was designed. We propose a novel control system in which a model predictive controller is used in real time to generate a reference trajectory for the UAV, which are then tracked by the nonlinear feedback controller. This combination allows to track predictions of the car motion with minimal position error. The evaluation presents three successful autonomous landings during the MBZIRC 2017, where our system achieved the fastest landing among all competing teams.     

Path‐following algorithms and experiments for an unmanned surface vehicle

This paper addresses the problem of path following in two‐dimensional space for underactuated unmanned surface vehicles (USVs), defining a set of guidance laws at the kinematic level. The proposed nonlinear Lyapunov‐based control law yields convergence of the path‐following error coordinates to zero. Furthermore, the introduction of a virtual controlled degree of freedom for the target to be followed on the path removes singularity behaviors present in other guidance algorithms proposed in the literature. Some heuristic approaches are then proposed to face the problem of speed of advance adaptation based on path curvature measurement and steering action prediction. Finally a set of experimental results of all the proposed guidance laws, carried out with the Charlie USV, demonstrates the feasibility of the proposed approach and the performance improvements, in terms of precision in following the reference path and transient reduction, obtained by introducing speed adaptation heuristics. © 2009 Wiley Periodicals, Inc.     

Partial camera automation in an unmanned air vehicle

The present study focused on an intelligent, semiautonomous, interface for a camera operator of a simulated unmanned air vehicle (UAV). This interface used system “knowledge” concerning UAV motion in order to assist a camera operator in tracking an object moving through the landscape below. The semiautomated system compensated for the translations of the UAV relative to the earth. This compensation was accompanied by the appropriate joystick movements ensuring tactile (haptic) feedback of these system interventions. The operator had to superimpose self-initiated joystick manipulations over these system-initiated joystick motions in order to track the motion of a target (a driving truck) relative to the terrain. Tracking data showed that subjects performed substantially better with the active system. Apparently, the subjects had no difficulty in maintaining control, i.e. “following” the active stick while superimposing self-initiated control movements over the system-interventions. Furthermore, tracking performance with an active interface was clearly superior relative to the passive system. The magnitude of this effect was equal to the effect of update-frequency (2-5 Hz) of the monitor image. The benefits of update frequency enhancement and semiautomated tracking were the greatest under difficult steering conditions. Mental workload scores indicated that, for the difficult tracking-dynamics condition, both semiautomation and update frequency increase resulted in less experienced mental effort. For the easier dynamics this effect was only seen for update frequency     

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.     

Finding a risk-constrained shortest path for an unmanned combat vehicle

We consider a problem of finding a reconnaissance route of an unmanned combat vehicle (UCV) in a terrain, which is modeled as a grid. It is assumed that the traverse time to pass through each cell in the grid and risk level associated with each cell are given and that the cells where the reconnaissance points to be visited by the UCV are located and the visiting sequence of such cells are given in advance as in real situation of military operation. We focus on the problem with the objective of minimizing the total travel time of the UCV for a given limit on the sum of risk level values associated with the cells on the path of the UCV. We develop an optimal solution algorithm based on a dynamic programming algorithm for multiple-choice knapsack problems. We also present a heuristic algorithm, which can be used for large-size problems. For evaluation of the performance of the proposed algorithms, computational experiments are performed on a number of problem instances, and results show that the proposed algorithms give optimal or good solutions within an acceptable time for real military operations.     

Heuristics for determining a patrol path of an unmanned combat vehicle

We consider a problem of finding a path of an unmanned combat vehicle that patrols a given area by visiting a given set of checkpoints with the objective of minimizing possibility of enemy’s infiltration. In this study, we focus on a situation in which the possibility of enemy’s infiltration at (through) each checkpoint is increased nonlinearly as time passes and the checkpoint may be patrolled multiple times during a planning horizon. We develop two-phase heuristics in which an initial path is constructed in the first phase and then it is improved in the second phase. For evaluation of the performance of the proposed heuristics, computational experiments are performed on randomly generated problem instances, and results show that the heuristics give good solutions in a reasonably short time.     

Neural Network Based Feedback Linearization Control of an Unmanned Aerial Vehicle

This paper presents a flight control design for an unmanned aerial vehicle (UAV) using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback linearization and output redefinition technique.The UAV investigated is non- minimum phase.The output redefinition technique is used in such a way that the resulting system to be inverted is a minimum phase system.The NARMA-L2 neural network is trained off-line for forward dynamics of the UAV model with redefined output and is then inverted to force the real output to approximately track a command input.Simulation results show that the proposed approaches have good performance.     

无人飞行器航迹规划研究

航迹规划的目的是要利用地形和敌情等信息,规划出生存概率最大的无人飞行器突防轨迹。通过对航迹规划任务的仿真需求分析,对无人飞行器的航迹规划进行了研究。首先根据遍布威胁的战场环境,构造了基于威胁源的Voronoi图,得到规避威胁的航迹路线;然后采用Dijkstra算法,搜索出最优航迹路线;最后利用Visual Studio .Net 2010开发平台,在MS SQL Server 2008数据库支持下,运用Visual C# 2008编制图形化界面,设计开发了无人飞行器航迹规划仿真系统,并给出了开发结果,实现了仿真结果的图形显示,为进一步的研究航迹规划奠定了基础。     

无人机吊挂飞行的非线性控制方法设计

针对四旋翼无人机吊挂飞行系统,本文设计了一种新型控制策略.本文首先建立了四旋翼无人机吊挂系统的数学模型.其中负载被看作由刚性绳悬挂在四旋翼无人机重心位置的质点.之后本文通过能量分析的方法设计了针对此系统的非线性控制器.本文提出的控制方法可以在抑制吊挂负载摆动的同时将四旋翼无人机移动到目标位置.本文运用李雅普诺夫稳定性分析和拉塞尔不变性原理对闭环系统的稳定性进行了证明.最后,通过数值仿真,分别将本控制器镇定控制和调节控制的控制效果与线性二次调节器(linear-quadratic regulator,LQR)控制器进行了对比.     

Pareto optimum design of an adaptive robust backstepping controller for an unmanned aerial vehicle

This research develops an adaptive robust backstepping (AR-backstepping) controller for stabilization of an unmanned aerial vehicle (UAV) having strong coupling and highly nonlinear dynamics. At first, the backstepping control method as the basic stabilizer is utilized to determine the control efforts of the considered UAV. Next, an adaptive-robust mechanism subject to gradient decent methods and sliding surfaces is implemented to regulate the control gains. In fact, this mechanism determines the speed of changes of the gain values of the backstepping controller to make better responses in the presence of disturbances and uncertainties. Then, the optimum values of the design parameters related to the adaptive-robust mechanism are selected by using a multi-objective ant-lion optimization (MOALO) algorithm to simultaneously minimize the total error and control efforts. Finally, the results for a UAV developed in the Sirjan University of Technology, Sirjan, Iran, are given to confirm the effectiveness, robustness, and advantages of the designed AR-backstepping controller.     

Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.     

Energy-based guidance of an underactuated unmanned underwater vehicle on a helical trajectory

This paper presents a motion control system for guidance of an underactuated Unmanned Underwater Vehicle (UUV) on a helical trajectory. The control strategy is developed using Port-Hamiltonian theory and interconnection and damping assignment passivity-based control. Using energy routing, the trajectory of a virtual fully actuated plant is guided onto a vector field. A tracking controller is then used that commands the underactuated plant to follow the velocity of the virtual plant. An integral control is inserted between the two control layers, which adds robustness and disturbance rejection to the design.     

人工蜂群算法的无人机航路规划与平滑

航路规划是无人机(UAV)作战任务规划系统的关键组成部分,目标是在适当的时间内为UAV计算出最优或次最优的飞行航路.人工蜂群(ABC)算法是一种最新发展的模拟昆虫王国中蜜蜂群体寻找优良蜜源的群体智能优化算法.采用人工蜂群算法完成无人机的平滑航路规划,首先阐述了人工蜂群算法的基本原理,然后将无人机航路规划问题通过建模转换成为一个多维函数优化问题,利用人工蜂群算法的优势,找到多维函数的最优解,最后对优化后的航路进行了平滑,使UAV对规划后的航路可飞.仿真实验结果表明,此方法可有效规划出航路,且所规划的航路可飞.     

Asymptotically stable path following for lateral motion of an unmanned ground vehicle

This study proposes an asymptotically stable path following controller for autonomous navigation of an unmanned ground vehicle (UGV) using vector field and robust-integral-signum error (RISE) feedback. The path following controller is divided into two parts: one part generating a heading command and another part designing a robust control. To determine the reference heading command under various uncertainties, the vector field method is employed, and then the RISE feedback controller is designed to follow the heading command. Finally, experiments are conducted on paved and unpaved roads to validate the effectiveness of the proposed method.     

类脑智能技术在无人系统上的应用

随着人工智能与脑科学等前沿技术的迅速发展,无人系统智能化研究正逐渐成为当今世界强国重点关注的战略发展方向,研究与之相关的科学问题具有前瞻性、战略性和带动性.文章首先分析了无人系统的发展需求,提出了面向需求的若干关键问题,包括复杂环境与态势信息的感知与认知问题、整体效能最优的分布式任务决策问题、面向任务需求的路径实时规划问题、考虑高不确定环境的自学习控制问题、应对非预期情况的故障诊断及容错问题以及基于人机接口的人机交互问题;随后,系统阐述了类脑智能技术在解决这些问题上的国内外研究现状;最后,论述了无人系统类脑智能化发展中依然存在的问题及未来发展趋势.     

Collision avoidance command governor for multi‐vehicle unmanned systems

In this paper, we consider the problem of coordinating a collection of autonomous unmanned vehicles while guaranteeing collision avoidance. Each vehicle is regulated by a local controller that ensures stability and provides desired path tracking performance in the absence of constraints. The fulfillment of coordination tasks (e.g., collision avoidance) and local constraints (e.g., input saturation constraints) is achieved through a command governor (CG) strategy that, whenever necessary, modifies the nominal paths of the vehicles. First, a centralized CG approach is proposed and fully analyzed. Then, a more interesting distributed implementation requiring low communication rates is discussed. Both approaches make use of a receding horizon strategy and require the on‐line solution of mixed‐integer optimization programs. Finally, an example is given for illustration purposes. Copyright © 2013 John Wiley & Sons, Ltd.     

A Java™ universal vehicle router for routing unmanned aerial vehicles

We consider vehicle routing problems in the context of the Air Force operational problem of routing unmanned aerial vehicles from base locations to various reconnaissance sites. The unmanned aerial vehicle routing problem requires consideration of heterogeneous vehicles, vehicle endurance limits, time windows, and time walls for some of the sites requiring coverage, site priorities, and asymmetric travel distances. We propose a general architecture for operational research problems, specified for vehicle routing problems, that encourages object‐oriented programming and code reuse. We create an instance of this architecture for the unmanned aerial vehicle routing problem and describe the components of this architecture to include the general user interface created for the operational users of the system. We employ route building heuristics and tabu search in a symbiotic fashion to provide a user‐defined level‐of‐effort solver interface. Empirical tests of solution algorithms parameterized for solution speed reveal reasonable solution quality is attained.     

小型无人地面车辆可视化仿真

以实验室中的小型无人地面车辆(SUGV)为背景,在MFC下利用OpenGL与3D Studio MAX进行该SUGV运动过程仿真,实现了三维可视化仿真场景系统中模型的建立、调入和视点的设置,在动力学分析基础上建立SUGV各种运动模型,实现了三维模型的交互控制.该仿真系统既可为操纵人员提供模拟操纵平台,也可为开发人员提供辅助设计和开发的平台.