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2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS)最新文献

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UAS Education at the French Civil Aviation University 法国民航大学的无人机教育
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999693
J. Sharples, G. Hattenberger
The French Civil Aviation University’s (ENAC, École Nationale de l’Aviation Civile) main objectives are to train personnel specialised in air transport, and to help these specialists adapt to civil aviation’s evolution, such as the advent of Unmanned Air Systems (UAS) in our skies. This paper shows that in ENAC, education in UAS is carried out in the same way than education in manned aviation topics. The training is divided in main subjects, which are then allocated to each student population according to their future roles: engineers, air traffic controllers, technicians, etc. Teaching is led by skilled personnel and backed up by modern facilities and equipment. This allows ENAC to provide a competent, specialised work force, ready to work on all aspects of the emerging and disruptive UAS-based operations.
法国民航大学(ENAC, École Nationale de l 'Aviation Civil)的主要目标是培养专门从事航空运输的人员,并帮助这些专家适应民用航空的发展,例如我们天空中无人驾驶航空系统(UAS)的出现。本文表明,在ENAC中,UAS教育的开展方式与载人航空主题教育相同。培训分为主要科目,然后根据他们未来的角色分配给每个学生群体:工程师、空中交通管制员、技术人员等。教学由技术人员领导,并以现代化的设施和设备为后盾。这使得ENAC能够提供一支有能力的专业工作队伍,随时准备在新兴的和破坏性的基于无人机的操作的各个方面工作。
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引用次数: 0
Optimal Guidance for Integrated Waypoint Following and Obstacle Avoidance 综合路点跟踪和避障的最优制导
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999721
Shaoming He, Hyo-Sang Shin, A. Tsourdos
This paper addresses the problem of energy-optimal waypoint-following guidance for an Unmanned Aerial Vehicle with the consideration of arbitrary number of obstacles. The proposed guidance law is derived as a solution of a linear quadratic optimal control problem in conjunction with convex parameter optimization. The algorithm developed integrates path following and obstacle avoidance into a single step and is able to be applied to a general waypoint-following mission. Several particular cases of the proposed guidance law are presented to provide better insights of the proposed algorithm. Nonlinear numerical simulations clearly demonstrate the effectiveness of the proposed formulations.
研究了考虑任意数量障碍物的无人机能量最优路径点跟随制导问题。该制导律是一个结合凸参数优化的线性二次最优控制问题的解。该算法将路径跟踪和避障集成到一个步骤中,能够应用于一般的路径点跟踪任务。为了更好地理解所提出的算法,给出了所提出的制导律的几个具体案例。非线性数值模拟清楚地证明了所提公式的有效性。
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引用次数: 1
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds 提高杂乱背景下目标检测和分类的学习效率
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999695
Vinorth Varatharasan, Hyo-Sang Shin, A. Tsourdos, Nick Colosimo
Usually, Neural Networks models are trained with a large dataset of images in homogeneous backgrounds. The issue is that the performance of the network models trained could be significantly degraded in a complex and heterogeneous environment. To mitigate the issue, this paper develops a framework that permit to autonomously generate a training dataset in heterogeneous cluttered backgrounds. It is clear that the learning effectiveness of the proposed framework should be improved in complex and heterogeneous environments, compared with the ones with the typical dataset. In our framework, a state-of-the-art image segmentation technique called DeepLab is used to extract objects of interest from a picture and Chroma-key technique is then used to merge the extracted objects of interest into specific heterogeneous backgrounds. The performance of the proposed framework is investigated through empirical tests and compared with that of the model trained with the COCO dataset. The results show that the proposed framework outperforms the model compared. This implies that the learning effectiveness of the framework developed is superior to the models with the typical dataset.
通常,神经网络模型是用均匀背景下的大量图像数据集来训练的。问题是,在复杂和异构的环境中,训练的网络模型的性能可能会显著下降。为了缓解这个问题,本文开发了一个框架,允许在异构杂乱背景下自主生成训练数据集。很明显,与典型数据集相比,所提出的框架在复杂和异构环境中的学习效率应该得到提高。在我们的框架中,使用最先进的图像分割技术DeepLab从图像中提取感兴趣的对象,然后使用Chroma-key技术将提取的感兴趣的对象合并到特定的异构背景中。通过实证测试考察了该框架的性能,并与COCO数据集训练的模型进行了比较。结果表明,所提出的框架优于模型。这意味着所开发的框架的学习效率优于具有典型数据集的模型。
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引用次数: 7
A Review of Existing Evaluation Methods for Point Clouds Quality 现有点云质量评价方法综述
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999725
H. Fretes, M. Gomez-Redondo, E. Paiva, J. Rodas, R. Gregor
One problem in 3D reconstruction from aerial photographs is the evaluation of the point clouds quality. For point clouds, in general, different authors evaluate their results in different ways. This paper analyzes the existing evaluation methods for the point cloud quality and a new discussion regarding their applicability to aerial photographs is opened. Some of these methods are chosen based on practical issues and applied to a pair of reconstructions. The principal conclusion is that objective methods are the most interesting in photogrammetry applications, particularly the comparison between two point clouds.
航拍图像三维重建中的一个问题是点云质量的评价。对于点云,一般来说,不同的作者以不同的方式评估他们的结果。本文分析了现有的点云质量评价方法,并对其在航空照片中的适用性进行了新的探讨。其中一些方法是根据实际问题选择的,并应用于一对重建。主要结论是,客观方法在摄影测量应用中最有趣,特别是两点云之间的比较。
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引用次数: 3
Teleoperation of a drone based on a virtual environment* 基于虚拟环境的无人机远程操作*
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999714
Belem Rojas, H. Abaunza, P. Castillo, I. Thouvenin, R. Lozano
In this paper, a teleoperation system for a quadrotor using a virtual environment is presented. A virtual aerial vehicle was introduced by applying the dynamic model of a quadrotor. An attitude controller was implemented for an operator to pilot the drone. The behavior of the virtual drone is communicated to a real vehicle through a mapping between the real and virtual spaces. This mapping is introduced in a robust quaternion-based controller ensures a safe flight in the real world. Simulations and experimental results are carried out to validate the proposal. With this idea of controlling a drone virtually, the performance of beginner and expert drone pilots is improved.
介绍了一种基于虚拟环境的四旋翼无人机远程操作系统。应用四旋翼飞行器的动力学模型,提出了一种虚拟飞行器。为操作人员驾驶无人机,实现了姿态控制器。虚拟无人机的行为通过真实和虚拟空间之间的映射传达给真实的车辆。这种映射被引入到鲁棒四元数控制器中,以确保在现实世界中安全飞行。仿真和实验结果验证了该方案的有效性。有了这种虚拟控制无人机的想法,初学者和专家无人机飞行员的性能得到了提高。
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引用次数: 0
Quadrotor System Design for a 3 DOF platform based on Iterative Learning Control 基于迭代学习控制的三自由度平台四旋翼系统设计
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999691
Husam A. Foudeh, P. Luk, J. Whidborne
Research into autonomous control and behaviour of mobile vehicles has become more and more widespread. Unmanned aerial vehicles (UAVs) have seen an upsurge of interest and of the many UAVs available, the quadrotor has shown significant potential in monitoring and surveillance tasks. This paper examines the performance of iterative learning control (ILC) in gradient-based control that enhances a quadrotor’s controllability and stability during attitude control. It describes the development of the learning algorithms which exploit the repeated nature of the fault-finding task. Iterative learning control algorithms are derived and implemented on a quadrotor in a test bench. The proposed ILC algorithms on the quadrotor model are evaluated for system stability, convergence speed, and trajectory tracking error. Finally, the performance of the proposed algorithms is compared against a baseline performance of the PID control schemes.
对移动车辆自主控制和行为的研究越来越广泛。无人驾驶飞行器(uav)已经看到了兴趣的高涨和许多可用的无人机,四旋翼在监测和监视任务中显示出显着的潜力。本文研究了迭代学习控制(ILC)在梯度控制中的性能,以提高四旋翼飞行器姿态控制的可控性和稳定性。它描述了利用故障查找任务的重复性质的学习算法的发展。推导了迭代学习控制算法,并在四旋翼试验台上实现了该算法。对四旋翼模型上的ILC算法进行了系统稳定性、收敛速度和轨迹跟踪误差的评估。最后,将所提出算法的性能与PID控制方案的基准性能进行比较。
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引用次数: 4
Distributed Multi-Target Tracking with D-DBSCAN Clustering 基于D-DBSCAN聚类的分布式多目标跟踪
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999712
Shuoyuan Xu, Hyo-Sang Shin, A. Tsourdos
This paper proposes a novel clustering-based distributed multi-target tracking algorithm over a sensor network. Each local sensor runs a joint probabilistic data association filter to obtain local state estimation. The estimates are communicated between connected sensors for track-totrack association and fusion. A novel distributed DBSCAN (D-DBSCAN) clustering algorithm is proposed to solve the track-to-track association problem. The proposed algorithm shows advantages in computational efficiency compared with conventional distributed multi-target tracking approaches. Extensive simulations provided substantial evidence for the effectiveness of the proposed algorithm.
提出了一种基于聚类的传感器网络分布式多目标跟踪算法。每个局部传感器运行一个联合概率数据关联滤波器来获得局部状态估计。这些估计值在连接的传感器之间进行通信,用于轨道间的关联和融合。针对航迹间关联问题,提出了一种新的分布式DBSCAN (D-DBSCAN)聚类算法。与传统的分布式多目标跟踪方法相比,该算法在计算效率方面具有优势。大量的仿真实验证明了该算法的有效性。
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引用次数: 0
A multi-UAS system for the inspection of photovoltaic plants based on the ROS-MAGNA framework 基于ROS-MAGNA框架的多无人机光伏电站检测系统
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999697
Alejandro Castillejo-Calle, Jose A. Millan-Romera, Héctor Pérez-Leon, J. L. Andrade-Pineda, I. Maza, A. Ollero
This paper presents the first application of the multi-purpose ROS-MAGNA [1] framework to a real inspection mission performed with a team of multiple UAS. This framework makes transparent the type of autopilot on-board and creates the state machines that control the behaviour of the different UAS from the specification of the multi-UAS mission. In addition, it integrates a virtual world generation tool to manage the environment’s information and visualize the geometrical objects of interest to properly follow the progress of the mission. Due to its flexibility, it can be applied to the inspection of different photovoltaic plants with teams of UAS. The paper describes the system architecture and the human machine interface developed for the mission management by the personnel of the photovoltaic plant. Finally, we show both simulations results and real flight experiments on a photovoltaic plant located in Utrera (Spain).
本文首次将多用途ROS-MAGNA[1]框架应用于由多个无人机组成的团队执行的实际检查任务。该框架使机载自动驾驶仪的类型变得透明,并创建了控制不同无人机行为的状态机,这些状态机与多无人机任务的规范不同。此外,它还集成了一个虚拟世界生成工具来管理环境信息和可视化感兴趣的几何对象,以正确地跟踪任务的进展。由于它的灵活性,它可以应用于不同的光伏电站与UAS团队的检查。介绍了为实现光伏电站人员任务管理而开发的系统体系结构和人机界面。最后,我们展示了位于西班牙乌特雷拉的光伏电站的模拟结果和真实飞行实验。
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引用次数: 4
Recursive Time-to-go Estimator for Anti-ship Missiles Guided by Pure Proportional Navigation 纯比例导航反舰导弹的递归剩余时间估计
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999701
W. Ra, Hyo-Sang Shin, Yunha Lee, I. Whang
This paper proposes a novel time-to-go estimation filter necessary for implementing the precise vertical homing guidance law of an anti-ship missiles with a RF seeker. By investigating the behavior of a pure proportional navigation guidance, it is shown that the time-to-go can be expressed as a Gauss hypergeometric function of the relative range, closing velocity and heading error. As an effective way to solve the time-to-go estimation problem, a recursive time-to-go estimation scheme with two-stage filter structure is taken into account. The suggested approach is able to not only consider the length of the curved homing trajectory caused by the heading error but also mitigate the unwanted time-to-go estimation errors due to the correlations among seeker measurement noises. Through the simulations for typical anti-ship missile homing scenarios, it is demonstrated that our method could provide superior time-to-go estimation performance compared to the previous ones.
本文提出了一种新的预估滤波器,用于实现带射频导引头的反舰导弹精确垂直寻的制导律。通过研究纯比例导航制导的行为,证明了剩余时间可以表示为相对距离、接近速度和航向误差的高斯超几何函数。作为解决剩余时间估计问题的一种有效方法,提出了一种递归的两级滤波器结构剩余时间估计方案。该方法不仅考虑了由航向误差引起的寻的轨迹弯曲长度,而且减轻了由于导引头测量噪声之间的相关性而产生的剩余时间估计误差。通过对典型反舰导弹寻的仿真,表明该方法具有较好的预估性能。
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引用次数: 2
Linear Quadratic Tracker with Integrator using Integral Reinforcement Learning 基于积分强化学习的线性二次跟踪器
Pub Date : 2019-11-01 DOI: 10.1109/REDUAS47371.2019.8999679
On Park, Hyo-Sang Shin, A. Tsourdos
This paper describes a Reinforcement Learning (RL) application using Linear Quadratic Regulator (LQR) based tracking controller, which is augmented with a tracking error term. In order to deal with the steady-state errors, Linear Quadratic Tracker with Integrator (LQTI) is designed by adding an integration term of the tracking error in the state variable. Based on the LQTI, an online learning using the Integral Reinforcement Learning (IRL) is applied for the tracking problem to find the optimal control on the partially unknown continuous-time systems by regulating the augmented state variable. The optimal control solution and the performance of the method are verified through numerical simulation on two applications.
本文描述了一种基于线性二次调节器(LQR)的跟踪控制器的强化学习(RL)应用,该控制器增加了跟踪误差项。为了处理稳态误差,在状态变量中加入跟踪误差的积分项,设计了带积分的线性二次跟踪器(LQTI)。在LQTI的基础上,利用积分强化学习(IRL)的在线学习方法,通过调节增广状态变量,找到部分未知连续系统的最优控制。通过两个应用的数值仿真验证了该方法的最优控制解和性能。
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引用次数: 2
期刊
2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS)
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