Fuzzy distributed cooperative tracking for a swarm of unmanned aerial vehicles with heterogeneous goals

This article proposes a systematic analysis for a tracking problem which ensures cooperation amongst a swarm of unmanned aerial vehicles (UAVs), modelled as nonlinear systems with linear and angular velocity constraints, in order to achieve different goals. A distributed Takagi–Sugeno (TS) framework design is adopted for the representation of the nonlinear model of the dynamics of the UAVs. The distributed control law which is introduced is composed of both node and network level information. Firstly, feedback gains are synthesised using a parallel distributed compensation (PDC) control law structure, for a collection of isolated UAVs; ignoring communications among the swarm. Then secondly, based on an alternation-like procedure, the resulting feedback gains are used to determine Lyapunov matrices which are utilised at network level to incorporate into the control law, the relative differences in the states of the vehicles, and to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is performed using tools from linear control theory: in particular the design criteria are posed as linear matrix inequalities (LMIs). An example based on a UAV tracking scenario is included to outline the efficacy of the approach.     

Robotic technologies for solar‐powered UAVs: Fully autonomous updraft‐aware aerial sensing for multiday search‐and‐rescue missions

Large‐scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high‐performance low‐altitude solar‐powered unmanned aerial vehicles (UAVs). However, today these UAVs suffer from small payload capacity, low energetic margins, and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar‐powered UAV system: First, a lightweight and power‐efficient day/night‐capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar‐powered UAVs. Fourth, as a main contribution an extended Kalman filter (EKF)‐based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end‐to‐end integration of the thermal‐induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft's low‐power autopilot. The complete system is verified during a 26 h search‐and‐rescue aerial sensing mock‐up mission that represents the first‐ever fully autonomous perpetual endurance flight of a small solar‐powered UAV with a day/night‐capable sensing payload. It also represents the first time that solar‐electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease‐of‐use, effectivity, and reliability in future multiday aerial sensing operations with small solar‐powered UAVs.     

Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV’s speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target’s speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.     

Robust kernelized correlation filter with scale adaption for real-time single object tracking

Kernelized correlation filter (KCF) is a kind of efficient method for real-time tracking, but remains being challenged by the drifting problem due to inaccurate localization caused by the scale variation and wrong candidate selection. In this paper, we propose a new scale adaptive kernelized correlation filter tracker, termed as SKCF, which estimates an accurate scale and models the distribution of correlation response to address the template drifting problem. In SKCF, a scale adaption method is used to find an accurate candidate. Thus we improve its capacity to drastic scale change which usually happens for unmanned aerial vehicles (UAVs)-based applications. The SKCF also introduces a Gaussian distribution to model the correlation response of the target image to select a better candidate in tracking procedure. Extensive experiments are performed on two commonly used tracking benchmarks and also a new benchmark for UAV tracking with complex scale variations. The results show that the proposed SKCF significantly improves the performance compared to the baseline KCF and achieves better performance than state-of-the-art single object trackers at real-time.     

Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, heading rates and speeds.     

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.     

Maximizing Water Surface Target Localization Accuracy Under Sunlight Reflection with an Autonomous Unmanned Aerial Vehicle

Reflected sunlight can significantly impact the effectiveness of vision-based object detection and tracking algorithms, especially ones developed for an aerial platform operating over a marine environment. These algorithms often fail to detect water surface objects due to sunlight glitter or rapid course corrections of unmanned aerial vehicles (UAVs) generated by the laws of aerodynamics. In this paper, we propose a UAV path planning method that maximizes the stationary or mobile target detection likelihood during localization and tracking by minimizing the sunlight reflection influences. In order to better reduce sunlight reflection effects, an image-based sunlight reflection reception adjustment is also proposed. We validate our method using both stationary and mobile target tracking tests.     

Autonomous UAV path planning and estimation

Unmanned aerial vehicles (UAVs) have shown promise in recent years for autonomous sensing. UAVs systems have been proposed for a wide range of applications such as mapping, surveillance, search, and tracking operations. The recent availability of low-cost UAVs suggests the use of teams of vehicles to perform sensing tasks. To leverage the capabilities of a team of vehicles, efficient methods of decentralized sensing and cooperative path planning are necessary. The goal of this work is to examine practical control strategies for a team of fixed-wing vehicles performing cooperative sensing. We seek to develop decentralized, autonomous control strategies that can account for a wide variety of sensing missions. Sensing goals are posed from an information theoretic standpoint to design strategies that explicitly minimize uncertainty. This work proposes a tightly coupled approach, in which sensor models and estimation objectives are used online for path planning.     

指定域多无人机协同目标跟踪研究

由于战场环境日益复杂、对抗性日益增强、任务日益多样和单机能力受限特性,多无人机协同执行作战任务已经成为无人机系统应用的重要发展趋势;针对协同作战中的系统复杂性、时间敏感性和通信计复杂性等特点进行分析,结合无人机运动性能及自主协同控制能力提出指定域多无人机协同目标跟踪的研究模型;首先建立面向战术任务的多无人机协同目标跟踪模型,其次在指定域范围内对多无人机进行面向持续跟踪模型的优化,最后选取一定数量的无人机在指定域内进行仿真,实验结果表明：面向持续的指定域多无人机协同目标跟踪模型是有效的,且具有较好的目标状态估计性能。     

Fixed-time backstepping distributed cooperative control for multiple unmanned aerial vehicles

This paper proposes a fixed-time backstepping distributed cooperative control scheme based on fixed-time extended state observer (FxTESO) for multiple unmanned aerial vehicles (UAVs). A fixed-time ESO, which is convergent independently of initial conditions, is designed to estimate and compensate the external disturbances in tracking process. Moreover, to eliminate the “explosion of complexity” in the traditional backstepping architecture, a nonlinear first-order filter is adopted to construct the distributed fixed-time control scheme. Based on the local information of neighboring UAVs, a fixed-time backstepping cooperative controller is designed. The proposed formation algorithm can be shown practicable for the UAV control system by using of Lyapunov stability theory and graph theory. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.     

A distributed architecture for a robotic platform with aerial sensor transportation and self‐deployment capabilities

This paper presents the architecture developed in the framework of the AWARE project for the autonomous distributed cooperation between unmanned aerial vehicles (UAVs), wireless sensor/actuator networks, and ground camera networks. One of the main goals was the demonstration of useful actuation capabilities involving multiple ground and aerial robots in the context of civil applications. A novel characteristic is the demonstration in field experiments of the transportation and deployment of the same load with single/multiple autonomous aerial vehicles. The architecture is endowed with different modules that solve the usual problems that arise during the execution of multipurpose missions, such as task allocation, conflict resolution, task decomposition, and sensor data fusion. The approach had to satisfy two main requirements: robustness for operation in disaster management scenarios and easy integration of different autonomous vehicles. The former specification led to a distributed design, and the latter was tackled by imposing several requirements on the execution capabilities of the vehicles to be integrated in the platform. The full approach was validated in field experiments with different autonomous helicopters equipped with heterogeneous devices onboard, such as visual/infrared cameras and instruments to transport loads and to deploy sensors. Four different missions are presented in this paper: sensor deployment and fire confirmation with UAVs, surveillance with multiple UAVs, tracking of firemen with ground and aerial sensors/cameras, and load transportation with multiple UAVs. © 2011 Wiley Periodicals, Inc.     

基于光照自适应动态一致性的无人机目标跟踪

无人机跟踪任务经常面临各种光线变化场景,然而无人机跟踪方法主要在光线充足下实现鲁棒跟踪。提出一种具有光照自适应性和跨帧语义感知动态一致性评估的无人机跟踪方法,实现光线不足下的无人机目标跟踪。首先构建光照自适应模块对昏暗场景进行识别,对视频图像的光照强度进行补偿;其次构建目标模板训练具有目标感知能力的滤波器进行相关运算,并利用跨帧之间的响应信息进行一致性评估;最后构建动态约束策略并对响应差异进行约束,使跟踪器保持时间平滑。在UAVDark135和UAV123数据集上,与9种先进算法进行对比实验,结果表明该算法具有较好的跟踪性能。     

面向目标跟踪的多机协同通信保持控制

由于无人机存在通信和测量约束的情况,远程无人机执行持续目标跟踪任务时无法直接与地面站保持通信,需要其他无人机作为通信中继方可与地面站建立可靠的通信连接.基于Dubins曲线,采用最小转弯半径和航向调整相结合的方法对具有初始和终止航向角约束的多无人机进行协同航路规划,确保所有无人机同时到达指定位置,形成多机协同通信保持的初始构型.针对随机移动目标,在多机协同通信保持的动态过程中,考虑平台性能、通信约束、碰撞规避等约束条件,采用非线性模型预测控制(NMPC)实现无人机协同分布式在线优化.在确保无人机通信中继保持的前提下,有效提高了算法的实时性.仿真结果表明了该算法的有效性.     

基于虚拟结构法的分布式多无人机鲁棒编队控制

针对多无人机编队控制(UAVs)问题, 本文提出了一种基于虚拟结构法的非线性鲁棒控制算法. 首先将编队 控制问题转化成两个子问题: 在惯性坐标系下虚拟刚体(VRB)光滑轨迹的生成设计, 以及在虚拟刚体坐标系下的无 人机编队队形控制设计. 针对部分无人机无法直接获取虚拟刚体状态的约束, 通过引入相邻无人机的跟踪状态, 实 现了分布式的编队控制. 同时, 考虑多无人机近距离编队飞行时相互间的气流扰动影响, 设计了基于supertwisting 的鲁棒控制算法, 提高了编队系统的控制精度和稳定性. 利用Lyapunov稳定性分析方法, 证明了位置跟踪误差在 有限时间内收敛到滑模面, 得到闭环系统全局渐近稳定的结果. 最后通过实际飞行实验, 验证了所提控制算法的有 效性和鲁棒性.     

Optimal scheduling for refueling multiple autonomous aerial vehicles

The scheduling, for autonomous refueling, of multiple unmanned aerial vehicles (UAVs) is posed as a combinatorial optimization problem. An efficient dynamic programming (DP) algorithm is introduced for finding the optimal initial refueling sequence. The optimal sequence needs to be recalculated when conditions change, such as when UAVs join or leave the queue unexpectedly. We develop a systematic shuffle scheme to reconfigure the UAV sequence using the least amount of shuffle steps. A similarity metric over UAV sequences is introduced to quantify the reconfiguration effort which is treated as an additional cost and is integrated into the DP algorithm. Feasibility and limitations of this novel approach are also discussed.     

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all (WTA) model is proposed for target tracking and cooperative competition of multi-UAVs (unmanned aerial vehicles). In this model, firstly, based on the artificial potential field method, the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets. Secondly, according to the finite-time convergence high-order differentiator, a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory. Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking, high precision, strong stability and avoiding chattering. Finally, a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy. Theoretical analysis and numerical simulation results show that the model has the fast convergence, high control accuracy, strong stability and good robustness.      

Vehicle detection and tracking in airborne videos by multi-motion layer analysis

Airborne vehicle detection and tracking systems equipped on unmanned aerial vehicles (UAVs) are receiving more and more attention due to their advantages of high mobility, fast deployment and large surveillance scope. However, such systems are difficult to develop because of factors like UAV motion, scene complexity, and especially the partial occlusion of targets. To address these problems, a new framework of multi-motion layer analysis is proposed to detect and track moving vehicles in airborne platform. After motion layers are constructed, they are maintained over time for tracking vehicles. Most importantly, since the vehicle motion layers can be maintained even when the vehicles are only partially observed, the proposed method is robust to partial occlusion. Our experimental results showed that (1) compared with other previous algorithms, our method can achieve better performance in terms of higher detection rate and lower false positive rate; (2) it is more efficient and more robust to partial occlusion; (3) it is able to meet the demand of real time application due to its computational simplicity.     

Survey on Computer Vision for UAVs: Current Developments and Trends

During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.     

多无人机目标追踪自适应控制

针对多无人机自动驾驶仪速度与航向角通道系数未知的目标轨迹追踪问题,提出一种自适应追踪控制方法.通过对自动驾驶仪通道系数进行在线估计,解决了系数未知所造成的设计困难.为克服外界干扰及系统内部误差因素对无人机运动控制系统的影响,设计了补偿项来消除干扰项的影响.采用Lyapunov定理证明了轨迹追踪误差最终可收敛于任意小的区域内.仿真结果验证了所设计方法的有效性.     

Aerial image mosaicking based on the 6-DoF imaging model

ABSTRACT

Image mosaicking technology is widely used in remote sensing and imaging of unmanned aerial vehicles (UAVs) for its superiority of generating large seamless images with high resolutions. Existing methods generally adopt the 8-DoF (degrees of freedom) homography camera model for image mosaicking, which require large computation cost in parameter optimization and cause distortions in the mosaicked images because of the accumulation errors. In view of this, this paper introduces an efficient mosaicking method based on 6-DoF imaging model to improve the quality of aerial image mosaicking. This derived model reduces the number of unknown variables for parameter optimization, hence improves the time efficiency. Meanwhile, a constraint term is merged into the objective function of optimization by analysing the characteristic of UAV imaging to eliminate the accumulation error in the mosaicking process. The experimental results on multiple datasets demonstrate that the proposed method achieves more accurate mosaicking results efficiently and is robust to different reference images relative to the state-of-the-art method.