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.     

Information-Measuring System for Unmanned Aerial Vehicle Payload Control

Journal of Computer and Systems Sciences International - The features of payload control are indicated on the example of an unmanned aerial vehicle equipped with an onboard radar station, when it...     

Tracking a Ground Moving Target with a Quadrotor Using Switching Control

An unmanned aerial vehicle (UAV) stabilization strategy based on computer vision and switching controllers is proposed. The main goal of this system is to perform tracking of a moving target on ground. The architecture implemented consists of a quadrotor equipped with an embedded camera which provides real-time video to a computer vision algorithm where images are processed. A vision-based estimator is proposed, which makes use of 2-dimensional images to compute the relative 3-dimensional position and translational velocity of the UAV with respect to the target. The proposed estimator provides the required states measurements to a micro-controller for stabilizing the vehicle during flight. The control strategy consists of switching controllers, which allows making decisions when the target is lost temporarily or when it is out of the camera’s field of view. Real time experiments are presented to demonstrate the performance of the target-tracking system proposed.     

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.     

Variable thrust directional control technique for plateau unmanned aerial vehicles高原型无人机变推力轴线控制技术研究

In order to increase the lift force of the unmanned aerial vehicles (UAV) in plateau areas, the UAV is commonly equipped with high span chord ratio wings. However, it may decrease the maneuverability of the aircraft, and thus increasing the risk of flight in complex terrain regions. Thrust vector control is a direct force flight control technique, which enhances the maneuverability and introduces the residual of the flight control system. In this paper, we develop a novel variable thrust direction mechanism, which provides the normal propeller UAV with the capability of directional force control. We propose a combinational flight control strategy for the newly developed UAV. Simulations and real flight test demonstrate the performance of the proposed technique in increasing the maneuverability of the conventional propeller UAV.     

UAV guidance using a monocular-vision sensor for aerial target tracking

Target tracking is difficult for a Unmanned Aerial Vehicle (UAV) equipped with a monocular-vision sensor because the sensor cannot measure the range between aerial target and UAV. Since the range between UAV and target is unobservable, the target position is also unknown. A measurement model of the vision sensor is proposed based on a specific image processing technique. A nonlinear adaptive observer is designed to estimate states and parameters, and the position of the target is estimated. A guidance law for target tracking and UAV maneuvers for persistent excitation condition are also proposed. To demonstrate the effectiveness of the proposed algorithms, numerical simulations are performed.     

Vision-based navigation of unmanned aerial vehicles

This paper presents a vision-based navigation strategy for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) using a single embedded camera observing natural landmarks. In the proposed approach, images of the environment are first sampled, stored and organized as a set of ordered key images (visual path) which provides a visual memory of the environment. The robot navigation task is then defined as a concatenation of visual path subsets (called visual route) linking the current observed image and a target image belonging to the visual memory. The UAV is controlled to reach each image of the visual route using a vision-based control law adapted to its dynamic model and without explicitly planning any trajectory. This framework is largely substantiated by experiments with an X4-flyer equipped with a fisheye camera.     

Cooperative Control of UAVs for Localization of Intermittently Emitting Mobile Targets

Compared with a single platform, cooperative autonomous unmanned aerial vehicles (UAVs) offer efficiency and robustness in performing complex tasks. Focusing on ground mobile targets that intermittently emit radio frequency signals, this paper presents a decentralized control architecture for multiple UAVs, equipped only with rudimentary sensors, to search, detect, and locate targets over large areas. The proposed architecture has in its core a decision logic which governs the state of operation for each UAV based on sensor readings and communicated data. To support the findings, extensive simulation results are presented, focusing primarily on two success measures that the UAVs seek to minimize: overall time to search for a group of targets and the final target localization error achieved. The results of the simulations have provided support for hardware flight tests.      

Dual–Authority Thrust–Vectoring of a Tri–TiltRotor employing Model Predictive Control

This paper addresses the exploitation of the combined potential of the directly-actuated and the underactuated control authorities of unmanned aerial vehicles with thrust-vectoring actuation. For the modeling, control synthesis and experimental evaluation a custom developed unmanned tri-tiltrotor is employed, equipped with rotor-tilting mechanisms which enable the direct actuation of its longitudinal dynamics, while retaining the standard body-pitching underactuated authority. An explicit model predictive control scheme relying on constrained multiparametric optimization is proposed for the dual-authority optimal control. The backbone of this scheme is a modeling representation that incorporates the separate internal dynamics of the two actuation principles and their interferences as they concurrently act on the free-flying vehicle body, while tractably representing their differentiated effects on the evolution of the longitudinal dynamics. This paper additionally presents the key implemented features that enable the autonomous operation of the employed tilt-rotor platform, in order to provide a reliable testbed for experimental evaluation. Finally, extensive experimental studies which conclusively validate this strategy’s increased efficiency are demonstrated.     

Aerial cooperative transporting and assembling control using multiple quadrotor–manipulator systems

In this paper, a fully distributed control scheme for aerial cooperative transporting and assembling is proposed using multiple quadrotor–manipulator systems with each quadrotor equipped with a robotic manipulator. First, the kinematic and dynamic models of a quadrotor with multi-Degree of Freedom (DOF) robotic manipulator are established together using Euler–Lagrange equations. Based on the aggregated dynamic model, the control scheme consisting of position controller, attitude controller and manipulator controller is presented. Regarding cooperative transporting and assembling, multiple quadrotor–manipulator systems should be able to form a desired formation without collision among quadrotors from any initial position. The desired formation is achieved by the distributed position controller and attitude controller, while the collision avoidance is guaranteed by an artificial potential function method. Then, the transporting and assembling tasks request the manipulators to reach the desired angles cooperatively, which is achieved by the distributed manipulator controller. The overall stability of the closed-loop system is proven by a Lyapunov method and Matrosov's theorem. In the end, the proposed control scheme is simplified for the real application and then validated by two formation flying missions of four quadrotors with 2-DOF manipulators.     

Dynamic Data Driven Application System for Plume Estimation Using UAVs

In this article, a full dynamic data-driven application system (DDDAS) is proposed for dynamically estimating a concentration plume and planning optimal paths for unmanned aerial vehicles (UAVs) equipped with environmental sensors. The proposed DDDAS dynamically incorporates measured data from UAVs into an environmental simulation while simultaneously steering measurement processes. In order to assimilate incomplete and noisy state observations into this system in real-time, the proper orthogonal decomposition (POD) is used to estimate the plume concentration by matching partial observations with pre-computed dominant modes in a least-square sense. In order to maximize the information gain, UAVs are dynamically driven to hot spots chosen based on the POD modes. Smoothed particle hydrodynamics (SPH) techniques are used for UAV guidance with collision and obstacle avoidance. We demonstrate the efficacy of the data assimilation and control strategies in numerical simulations. Especially, a single UAV outperforms the ten static sensors in this scenario in terms of the mean square error over the full time interval. Additionally, the multi-vehicle data collection scenarios outperform the single vehicle scenarios for both static sensors at optimal positions and UAVs controlled by SPH.     

Particle filter-based aerial tracking for moving targets

This paper considers the problem of tracking a moving target with a radio transmitter using an aerial robot in an online manner. The aerial robot is equipped with a low-cost directional antenna and Software Defined Radio receiver to obtain the signal emitted by the target. The aerial robot rotates around itself and collects a predefined number of signal recordings from each direction to determine the bearing angle to the target in which the received signal strength is maximized. The measurement uncertainty is assumed to be bounded and represented by two triangular areas divided by a bisector. To localize and track the target, a particle filter-based approach is proposed. In this approach, we integrate the discrete and bounded measurement model with the particle filter in such a way that the particles' weights are updated based on a novel method which considers the measurement wedge and the particle locations with respect to this wedge along with a logistic function. We also incorporate the doubling strategy into the particle filter to determine the next measurement locations and avoid arbitrarily large number of measurements. We choose wildlife monitoring as a use case scenario in which a radio transmitter is put on the animal under consideration to allow wildlife researchers to track it. Since each animal has its own motion behavior, we consider different motion models for the target, which are used in modeling animal movements in wildlife studies. Therefore, the proposed approach is validated using a target moving with varying velocity and acceleration. We verified the tracking performance of the approach through a series of extensive simulations. We compared the proposed approach with the optimal offline strategy in terms of the empirical competitive ratio of the total distance traveled and the tracking distance. We also developed a low-cost hardware platform and software infrastructure for the proposed tracking system. Using this platform, we conducted field experiments for the stationary and moving targets.     

Robust motion control of aerial manipulators

Aerial manipulators are composed of a robotic arm installed on an unmanned aerial vehicle and are used in several applications because of their inherent ability in performing complex tasks. In real-world applications, these systems are required to be robust against exogenous disturbances, such as wind, to guarantee the desired level of accuracy in the execution of the tasks. In this paper, the reference scenario consists of an aerial manipulator with a camera mounted on the end-effector of the robotic arm, and the goal is to track a fast-moving target. A control system architecture able to assure that the tracking error remains bounded even in the presence of external disturbances is illustrated. The proposed approach is based on the compensation of the dynamic coupling between the robotic arm and the unmanned aerial vehicle. Stability is analytically proved, and the effectiveness of the proposed control solution is shown with some simulations.     

Accurate Modeling and Robust Hovering Control for a Quad–rotor VTOL Aircraft

Quad-robot type (QRT) unmanned aerial vehicles (UAVs) have been developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV is equipped with four propellers driven by each electric motor, an embedded controller, an Inertial Navigation System (INS) using three rate gyros and accelerometers, a CCD (Charge Coupled Device) camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. Accurate modeling and robust flight control of QRT UAVs are mainly discussed in this work. Rigorous dynamic model of a QRT UAV is obtained both in the reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurately derived dynamics. The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method. Although a model is incorrect, DOB method can design a controller by regarding the inaccurate part of the model and sensor noises as disturbances. The UAV can also avoid obstacles using eight IR (Infrared) and four ultrasonic range sensors. This kind of micro UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment. The experimental results show the performance of the proposed control algorithm.     

基于神经网络的无人机滑模飞行控制设计

针对无人机受扰运动,基于Backstepping方法和非线性滑模控制提出了一种鲁棒神经网络飞行控制方案。对无人机姿态角速度层的系统不确定性项,采用径向基函数神经网络并对其权值进行在线调整,从而实现对其进行逼近。将回馈递推设计方法与滑模控制方法结合起来,基于神经网络的输出为无人机设计了一种回馈递推滑模飞行控制器。所设计的飞行控制器用于无人机的姿态控制,仿真结果表明所研究的无人机鲁棒神经网络飞行控制方案是有效的。     

Dynamic modeling and control for aerial arm-operating of a multi-propeller multifunction aerial robot

We proposed a multi-propeller multifunction aerial robot that is constructed by a quadrotor with two multi-DOF arms to enable aerial robotic operations. This paper addresses the dynamics and control problems for aerial arm-operation. The dynamic modeling considering the coupling between the arms and main-body subsystems is investigated using the Lagrange approach. The dynamics of the system are partitioned into the main-body dynamics, the arm dynamics, and the interaction dynamics. A composite controller consisting of a main-body sub-controller and an arm sub-controller are presented. Each sub-controller is designed based on the partitioned dynamics. The main-body sub-controller is designed using trajectory linearization control technique. This composite controller is appropriate for real-time implementation due to its simplicity. An optimal planning strategy that minimizes the interaction between main-body subsystem and arm subsystem is proposed. Experimental results are presented, verifying the effectiveness of the composite controller.     

悬挂伸缩刀具的树障清理空中机器人飞行控制方法

针对输电线路附近的树障进行清理问题,本文提出了一种新型的悬挂伸缩刀具的树障清理空中机器人并进行了仿真和实物验证.首先,对悬挂伸缩刀具的空中机器人进行了伸缩刀具重心变化下的动力学、运动学建模及接触建模.其次,为避免空中机器人接触作业时机器人倾翻的问题,设计了力估计器用于力感知和导纳控制器用于力控制.针对空中机器人非线性强耦合、伸缩刀具时参数摄动及作业时扰动的问题,设计了线性自抗扰控制(LADRC)的机器人位姿控制器.再次,数值仿真验证了导纳控制能有效避免空中机器人接触作业时产生倾翻的问题,以及基于LADRC控制器的位姿控制具有良好的稳定性和抗扰性.最后,通过实物飞行和接触作业测试,进一步验证了本文悬挂伸缩刀具的树障清理空中机器人及其控制方法的有效性.     

基于邻域一致性的无人机航拍图像配准过程控制技术

由于无人机航拍图像的采样节点重叠度较高,所以在无人机航拍图像配准过程中无法避免像素点错误匹配的问题,提出基于邻域一致性的无人机航拍图像配准过程控制技术。根据无人机航拍图像配准区域划定标准,识别关键图像标注点,通过计算一致性测度值指标设计基于邻域一致性的无人机航拍图像融合方法。结合图像融合结果进行航拍图像地理定位,根据采样灰度因子的数值水平分析图像采样节点之间的重叠关系,对航拍图像进行坐标变换与几何校正。结合坐标变换与几何校正结果提取图像边界特征点,将特征点作为配准过程控制点,根据控制点分布质量与尺度空间极值实现无人机航拍图像配准过程控制。实验结果表明,在邻域一致性原则作用下,无人机航拍图像采样节点之间的重叠度明显下降,像素点错误匹配问题得到了解决,配准效果好。     

Position and attitude control of multi-rotor aerial vehicles: A survey

Motion control theory applied to multi-rotor aerial vehicles (MAVs) has gained attention with the recent increase in the processing power of computers, which are now able to perform the calculations needed for this technique, and with lower cost of sensors and actuators. Control algorithms of this kind are applied to the position and the attitude of MAVs. In this paper, we present a review of recent developments in position control and attitude control of multi-rotor aerial robots systems. We also point out the growth of related research, starting with the boom in multi-rotor unmanned aerial robotics that began after 2010, and we discuss reported field applications and future challenges of the control problem described here. The objective of this survey is to provide a unified and accessible presentation, placing the classical model of a multi-rotor aerial vehicle and the proposed control approaches into a proper context, and to form a starting point for researchers who are initiating their endeavors in linear/nonlinear position, altitude or attitude control applied to MAVs. Finally, the contribution of this work is an attempt to present a comprehensive review of recent breakthroughs in the field, providing links to the most interesting and most successful works from the state-of-the-art.     

A Ground Control Station for a Multi-UAV Surveillance System

This paper presents the ground control station developed for a platform composed by multiple unmanned aerial vehicles for surveillance missions. The software application is fully based on open source libraries and it has been designed as a robust and decentralized system. It allows the operator to dynamically allocate different tasks to the UAVs and to show their operational information in a 3D realistic environment in real time. The ground control station has been designed to assist the operator in the challenging task of managing a system with multiple UAVs, trying to reduce his workload. The multi-UAV surveillance system has been demonstrated in field experiments using two quadrotors equipped with visual cameras.