基于知识库的无人机作战自主决策方法的研究

提高无人机的自主决策能力是提升无人机在现代战争中作战能力的重要手段。通过对无人机对地作战过程的研究,分析归纳影响无人机自主决策的相关因素,并将无法用数学模型描述的军事规则使用产生式规则来表达,建立作战规则库。然后提出一种基于知识库的自主决策方法,该方法通过动态贝叶斯网络模拟人对态势的认知,通过产生式规则进行决策,其中加入实体的状态描述来控制决策流程,以此来完成无人机的自主决策。仿真结果表明,该方法科学有效,可使无人机自主做出合理决策。     

Fuzzy Logic Based Approach to Design of Flight Control and Navigation Tasks for Autonomous Unmanned Aerial Vehicles

This paper proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed under the main navigation system for the control of the altitude, the speed, and the heading, through which the global position (latitude–longitude) of the air vehicle is controlled. A SID (Standard Instrument Departure) and TACAN (Tactical Air Navigation) approach is used and the performance of the fuzzy based controllers is evaluated with time based diagrams under MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six-degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.     

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.     

无人机动态路径规划可视化仿真系统研究

针对现阶段无人机动态路径规划算法仿真可视性差和飞行环境仿真不足等问题,提出了一种基于Cesium的无人机动态路径规划可视化仿真系统;该系统在基于Client/Server(B/S)架构基础上,使用开源三维虚拟地球Cesium可视化引擎,按照可视化系统的总体设计及流程,利用坐标系转换方法与三维可视化技术,构建了一个无人机在真实地理环境中动态路径规划可视化仿真系统,实现了三维人工势场法路径规划的动态可视化仿真展示;实验表明,该系统不仅能够直观展示无人机在真实地理环境下的动态路径规划过程效果,还能够为相关研究提供直观、全面的可视化分析和评估手段。     

Variable Resolution Search with Quadrotors: Theory and Practice

This paper presents a variable resolution framework for autonomously searching stationary targets in a bounded area. Theoretical formulations are also described for using a probabilistic quadtree data structure, which incorporates imperfect Bayesian (false positive and false negative) detections and informs the searcher's route based on optimizing information gain. Live‐fly field experimentation results using a quadrotor unmanned aerial vehicle validate the proposed methodologies and demonstrate an integrated system with autonomous control and embedded object detection for probabilistic search in realistic operational settings. Lessons learned from these field trials include characterization of altitude‐dependent detection performance, and we also present a benchmark data set of outdoor aerial imagery for search and detection applications.     

Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation

Classical haptic teleoperation systems heavily rely on operators’ intelligence and efforts in aerial robot navigation tasks, thereby posing significantly users’ workloads. In this paper, a novel shared control scheme is presented facilitating a multirotor aerial robot haptic teleoperation system that exhibits autonomous navigation capability. A hidden Markov model filter is proposed to identify the intention state of operator based on human inputs from haptic master device, which is subsequently adopted to derive goal position for a heuristic sampling based local path planner. The human inputs are considered as commanded velocity for a trajectory servo controller to drive the robot along the planned path. In addition, vehicle velocity is perceived by the user via haptic feedback on master device to enhance situation awareness and navigation safety of the user. An experimental study was conducted in a simulated and a physical environment, and the results verify the effectiveness of the novel scheme in safe navigation of aerial robots. A user study was carried out between a classical haptic teleoperation system and the proposed approach in the identical simulated complex environment. The flight data and task load index (TLX) are acquired and analyzed. Compared with the conventional haptic teleoperation scheme, the proposed scheme exhibits superior performance in safe and fast navigation of the multirotor vehicle, and is also of low task and cognitive loads.

     

Autonomous feature following for visual surveillance using a small unmanned aerial vehicle with gimbaled camera system

This paper represents the development of feature following control and distributed navigation algorithms for visual surveillance using a small unmanned aerial vehicle equipped with a low-cost imaging sensor unit. An efficient map-based feature generation and following control algorithm is developed to make an onboard imaging sensor to track a target. An efficient navigation system is also designed for real-time position and velocity estimates of the unmanned aircraft, which is used as inputs for the path following controller. The performance of the proposed autonomous path following capability with a stabilized gimbaled camera onboard a small unmanned aerial robot is demonstrated through flight tests with application to target tracking for real-time visual surveillance.     

四旋翼无人机飞控系统的研究与实现

本文通过数学建模进行动力学系统分析,研究实现了基于硬件和软件的四旋翼无人机飞控系统。首先、构建了四旋翼无人机动力学模型并进行理论分析;其次、设计了无人机机架,对各组成模块进行测试、分析和试验;再次、通过集成软硬件实现了无人机飞控系统并进行飞行测试;最后、实验结果表明,实现的无人机飞控系统取得了较好的飞控效果,具有灵敏性强、稳定性高,总体性能优良等优点。     

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.     

Parameter identification and design of a robust attitude controller using <Emphasis Type="Italic">H</Emphasis><Subscript>∞</Subscript> methodology for the raptor E620 small-scale helicopter

Rigorous control synthesis for an unmanned aerial vehicle necessitates the availability of a good, reasonable model for such a vehicle. The work reported in this paper focuses on the modeling of a rotary unmanned aerial vehicle (RUAV) and the development of a robust controller that can handle parameter uncertainties and disturbances. The parameters of the plant model are obtained through the use of the prediction error method with real flight data. The response of the identified linear model shows a good match with the measured flight data. The H _∞ control scheme is applied to obtain a robustly stable controller using the identified model. The proposed controller is analyzed using frequency-domain analysis and time-domain simulations. The performance of the proposed H _∞ controller is better than that of the conventional proportional derivative controller in that the proposed controller has a shorter settling time and less overshoot. Furthermore, the degradation of the proposed controller performance is negligible and stability is maintained when the input gains to the plant are doubled, which demonstrates the good performance and robustness of the controller.     

Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search

This paper discusses the results of a field experiment conducted at Savannah River National Laboratory to test the performance of several algorithms for the localization of radioactive materials. In this multirobot system, both an unmanned aerial vehicle, a custom hexacopter, and an unmanned ground vehicle (UGV), the ClearPath Jackal, equipped with γ‐ray spectrometers, were used to collect data from two radioactive source configurations. Both the Fourier scattering transform and the Laplacian eigenmap algorithms for source detection were tested on the collected data sets. These algorithms transform raw spectral measurements into alternate spaces to allow clustering to detect trends within the data which indicate the presence of radioactive sources. This study also presents a point source model and accompanying information‐theoretic active exploration algorithm. Field testing validated the ability of this model to fuse aerial and ground collected radiation measurements, and the exploration algorithm’s ability to select informative actions to reduce model uncertainty, allowing the UGV to locate radioactive material online.     

小型无人机三维导航控制研究

为提高小型无人机自主飞行的安全性能,更好地执行特定任务,在二维平面导航的基础上提出一种三维导航控制方法;给出三维导航控制器总体结构的设计,介绍了非定高下航程推算的原理,重点阐述了自主飞行中如何在平面导航基础上引入高度控制;利用Matlab/Simulink工具箱搭建仿真模型对三维导航控制律进行仿真验证;仿真结果表明,设计的控制方法切实可行,三维导航系统稳定性良好。     

高速数据传输单元

为了满足无人机数据链对下行数据高速实时传输的要求,实现了一种基于PowerPC处理器和IEEE1394总线的高速数据传输单元。首先介绍了无人机数据链对下行数据传输设备的设计需求,其次详细介绍了下行数据传输设备的系统结构和数据传输单元的软硬件实现,最后通过 IEEE1394总线环绕测试的方法,验证了数据传输单元的功能和性能。验证结果表明：该数据传输单元具有设计简单、传输速率高、可靠性高和功耗低的特点,具有广泛的应用前景。     

新息序列驱动的无人机控制系统数据攻击检测

伴随物联网和自主系统的不断发展,信息物理系统的网络安全备受关注.无人机是一种典型的依靠通信和控制系统实现自主飞行的智能装置,其安全性尤为突出.本文针对无人机的状态估计算法,考虑其传感器和控制指令受到数据攻击,提出基于扩展卡尔曼滤波的新息序列状态估计检测方法.首先建立无人机信息物理模型,引入状态估计算法和数据攻击模型.然后,利用新息序列构造标量检测统计量用于数据攻击检测,并针对飞行器机动造成的状态跳变引入负无穷范数,用以降低数据攻击检测的误检率.最后,通过仿真实验验证所提出的检测方法能有效检测不同威胁模式下和状态下无人控制系统的数据攻击.     

Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation

This paper reports on the statistical embedding of a structural pattern recognition system into the autonomous navigation of an unmanned aerial vehicle (UAV). A rule-based system is used for the recognition of visual landmarks such as bridges in aerial views. In principle, rule-based systems can be designed and coded with no training data at hand, but a sound interpretation and utilization of the achieved results needs statistical inference and representative data sets of sufficient coverages. Flying a UAV with an experimental system is expensive, risky, and legally questionable. Therefore, we chose a virtual globe as a camera simulator providing arbitrary amounts of training and test data. The expected positions of landmarks in the aerial views are modeled by mixture models representing inliers, outliers, and intermediate forms which stem from similar structures appearing frequently in the vicinity of landmarks. The parameters of the corresponding likelihood functions are estimated by the Expectation–Maximization method. Using these estimates, we carry out tests and compare the results for heuristic, pessimistic, optimistic, and Bayesian decision rationales. This performance evaluation reveals the superiority of the Bayesian approach.     

Secure Mobility and the Autonomous Driver

Autonomous mobility systems developed for unmanned ground vehicles may have additional benefits by enhancing system performance and reducing demands on operators for manned ground vehicles. This effort examines the potential impact of introducing autonomous mobility to control manned vehicles while operators performed secure mobility. Eleven Soldiers participated in an experimental task requiring concurrent control of a manned and an unmanned Stryker performing a road march, scanning of the local environment for targets, and planning of a reconnaissance route for a third simulated asset. The control of the manned vehicle was varied between autonomous and manual control and several speed and accuracy variables were examined for each task. Subjective measures of operator workload, stress, and motion sickness were also examined. The results support the potential benefits of incorporating autonomous mobility into manned platforms. In speed-matched conditions, autonomous mobility was associated with decreased manned vehicle mission time, faster operator reaction times to targets, greater instances of multitasking while under motion, and lower subjective operator workload measures than with manual driving. In conclusion, autonomous mobility technologies have the potential to free up resources from the vehicle operator and allow for better operator performance on tasks other than vehicle control.     

A novel hybrid particle swarm optimization for multi-UAV cooperate path planning

Applied Intelligence - The path planning of unmanned aerial vehicle (UAV) in three-dimensional (3D) environment is an important part of the entire UAV’s autonomous control system. In the...     

基于自动化接受度模型的车外交互研究

摘 要：随着科技的不断进步,新技术不断进入人们的生活,自动驾驶驾驶技术也得到了 快速的发展。但单纯的技术提高并不能带来人们接受度的随之增加。为了提高人们对无人驾驶 技术的接受度,对自动化接受度模型进行研究,探究如何从认知接受度提升的角度改善自动驾 驶座舱的人机交互,并从接受度模型的核心影响因素的角度分析车外人机交互用户体验问题。 通过分析半封闭场景下车外交互的使用场景及行人与无人车交互时的用户行为,归纳出行人行 为特征模型,再结合自动化接受度模型的相关研究,从信任度、协作性的角度提升车外交互的 有效性和认知接受度。基于自动化接受度 AAM 模型提出了符合行人行为特征和认知的车外人 机交互策略,从而提高沟通效率和接受度。     

Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) are becoming popular among researchers and vital platforms for several autonomous mission systems. In this paper, we present the design and development of a miniature autonomous rotorcraft weighing less than 700 g and capable of waypoint navigation, trajectory tracking, visual navigation, precise hovering, and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini‐ and microrotorcraft, an embedded and inexpensive autopilot was developed. To compensate for the weaknesses of the low‐cost equipment, we put our efforts into designing a reliable model‐based nonlinear controller that uses an inner‐loop outer‐loop control scheme. The developed flight controller considers the system's nonlinearities, guarantees the stability of the closed‐loop system, and results in a practical controller that is easy to implement and to tune. In addition to controller design and stability analysis, the paper provides information about the overall control architecture and the UAV system integration, including guidance laws, navigation algorithms, control system implementation, and autopilot hardware. The guidance, navigation, and control (GN&C) algorithms were implemented on a miniature quadrotor UAV that has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Experimental results that demonstrate the operation of the GN&C algorithms and the capabilities of our autonomous micro air vehicle are presented. © 2009 Wiley Periodicals, Inc.     

TrackerBots: Autonomous unmanned aerial vehicle for real‐time localization and tracking of multiple radio‐tagged animals

Autonomous aerial robots provide new possibilities to study the habitats and behaviors of endangered species through the efficient gathering of location information at temporal and spatial granularities not possible with traditional manual survey methods. We present a novel autonomous aerial vehicle system—TrackerBots—to track and localize multiple radio‐tagged animals. The simplicity of measuring the received signal strength indicator (RSSI) values of very high frequency (VHF) radio‐collars commonly used in the field is exploited to realize a low‐cost and lightweight tracking platform suitable for integration with unmanned aerial vehicles (UAVs). Due to uncertainty and the nonlinearity of the system based on RSSI measurements, our tracking and planning approaches integrate a particle filter for tracking and localizing and a partially observable Markov decision process for dynamic path planning. This approach allows autonomous navigation of a UAV in a direction of maximum information gain to locate multiple mobile animals and reduce exploration time and, consequently, conserve on‐board battery power. We also employ the concept of search termination criteria to maximize the number of located animals within power constraints of the aerial system. We validated our real‐time and online approach through both extensive simulations and field experiments with five VHF radio‐tags on a grassland plain.