UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. Their equipment and mission plan are carefully selected to minimize the carried load an overall resource consumption. Typically, several single task UAVs are dispatched to perform different missions. In certain cases, (part of) the geographical area of operation may be common to these single task missions (such as those supporting post-disaster recovery) and it may be more efficient to have multiple tasks carried out as part of a single UAV mission using common or even additional specialized equipment. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.
As overviewed in @cite_10 , fleets of UAVs operating as distributed processing systems can be adopted for various monitoring tasks, including, e.g., surveillance, object detection, movement tracking, support to navigation. A prototype of UAV-based architecture for sensing operations has been described in @cite_18 . In our paper, we consider a conceptually similar UAV equipment of hardware and software modules.
@cite_18 @cite_10
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Mobile wireless sensor networks have been extensively deployed for enhancing environmental monitoring and surveillance. The availability of low-cost mobile robots equipped with a variety of sensors makes them promising in target coverage tasks. They are particularly suitable where quick, inexpensive, or nonlasting visual sensing solutions are required. In this paper, we consider the problem of low complexity target tracking to cover and follow moving targets using flying robots. We tackle this problem by clustering targets while estimating the camera location and orientation for each cluster separately through a cover-set coverage method. We also leverage partial knowledge of target mobility to enhance the efficiency of our proposed algorithms. Three computationally efficient approaches are developed: predictive fuzzy , predictive incremental fuzzy , and local incremental fuzzy . The objective is to find a compromise among coverage efficiency, traveled distance, number of drones required, and complexity. The targets move according to one of the following three possible mobility patterns: random waypoint, Manhattan grid, and reference point group mobility patterns. The feasibility of our algorithms and their performance are also tested on a real-world indoor testbed called drone-be-gone , using Parrot AR.Drone quadcopters. The deployment confirms the results obtained with simulations and highlights the suitability of the proposed solutions for real-time applications. We consider the problem of having a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) pursue a second team of evaders while concurrently building a map in an unknown environment. We cast the problem in a probabilistic game theoretical framework, and consider two computationally feasible greedy pursuit policies: local-mar and global-max. To implement this scenario on real UAVs and UGVs, we propose a distributed hierarchical hybrid system architecture which emphasizes the autonomy of each agent, yet allows for coordinated team efforts. We describe the implementation of the architecture on a fleet of UAVs and UGVs, detailing components such as high-level pursuit policy computation, map building and interagent communication, and low-level navigation, sensing, and control. We present both simulation and experimental results of real pursuit-evasion games involving our fleet of UAVs and UGVs, and evaluate the pursuit policies relating expected capture times to the speed and intelligence of the evaders and the sensing capabilities of the pursuers. There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5 ) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time. The motivation of this research is to show that visual based object tracking and following is reliable using a cheap GPS-denied multirotor platform such as the AR Drone 2.0. Our architecture allows the user to specify an object in the image that the robot has to follow from an approximate constant distance. At the current stage of our development, in the event of image tracking loss the system starts to hover and waits for the image tracking recovery or second detection, which requires the usage of odometry measurements for self stabilization. During the following task, our software utilizes the forward-facing camera images and part of the IMU data to calculate the references for the four on-board low-level control loops. To obtain a stronger wind disturbance rejection and an improved navigation performance, a yaw heading reference based on the IMU data is internally kept and updated by our control algorithm. We validate the architecture using an AR Drone 2.0 and the OpenTLD tracker in outdoor suburban areas. The experimental tests have shown robustness against wind perturbations, target occlusion and illumination changes, and the system's capability to track a great variety of objects present on suburban areas, for instance: walking or running people, windows, AC machines, static and moving cars and plants. In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45 compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28 enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs. Distributed Processing Systems are the ones that include multiple devices (which could be of many types, such as PC computers, mobile devices etc.) that have computational and communication capabilities. Their computational power is jointly used for collaborative processing of variety of tasks – and this processing is realized in distributed manner. UAV - Unmanned Aerial Vehicles (also called drones) gain significant attention over recent years. They have been employed to realize multiple tasks such as surveillance or environmental monitoring. First implementations were based on single UAV, later the potential of multiple UAVs collaborating in a team was noticed. Many applications were implemented in distributed manner, using multiple collaborative UAVs and the distributed processing systems principles. In this paper we survey the applications implemented over cooperative teams of UAVs that operate as distributed processing systems. Urban search and rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. A limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors, and computing resources has to be found. Communication to external computers cannot be guaranteed; therefore, all processing and decision making has to be done on board. In this article, we present an unmanned aircraft system design fulfilling these requirements. The components of our system are structured into groups to encapsulate their functionality and interfaces. We use both laser and stereo vision odometry to enable seamless indoor and outdoor navigation. The odometry is fused with an inertial measurement unit in an extended Kalman filter. Navigation is supported by a module that recognizes known objects in the environment. A distributed computation approach is adopted to address the computational requirements of the used algorithms. The capabilities of the system are validated in flight experiments, using a quadrotor. In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs’ locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45 compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28 enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs. The objective of this paper is to explore the feasibility of using multiple low-altitude, short endurance (LASE) unmanned air vehicles (UAVs) to cooperatively monitor and track the propagation of large forest fires. A real-time algorithm is described for tracking the perimeter of fires with an on-board infrared sensor. Using this algorithm, we develop a decentralized multiple-UAV approach to monitoring the perimeter of a fire. The UAVs are assumed to have limited communication and sensing range. The effectiveness of the approach is demonstrated in simulation using a six degree-of-freedom dynamic model for the UAV and a numerical propagation model for the forest fire. Salient features of the approach include the ability to monitor a changing fire perimeter, the ability to systematically add and remove UAVs from the team, and the ability to supply time-critical information to fire fighters. Vehicle tracking from a moving aerial platform poses a number of unique challenges including the small number of pixels representing a vehicle, large camera motion, and parallax error. This paper considers a multi-modal sensor to design a real-time persistent aerial tracking system. Wide field of view (FOV) panchromatic imagery is used to remove global camera motion whereas narrow FOV hyperspectral image is used to detect the target of interest (TOI). Hyperspectral features provide distinctive information to reject objects with different reflectance characteristics from the TOI. This way the density of detected vehicles is reduced, which increases tracking consistency. Finally, we use a spatial data based classifier to remove spurious detections. With such framework, parallax effect in non-planar scenes is avoided. The proposed tracking system is evaluated in a dense, synthetic scene and outperforms other state-of-theart traditional and aerial object trackers. Recent advancements in manufacturing low-cost wireless battery operated cameras has made their application in Wireless Video Sensor Networks (WVSN) increasingly more feasible and affordable. The application of robotic sensing agents equipped with cameras in WVSNs, seems particularly promising in performing coverage tasks for ad hoc surveillance. Their application in this context can be specifically useful for surveying areas with little to no available or affordable infrastructure, or where quick deployment is necessary. In this paper, we address the target coverage problem for finding the minimum number of cameras, their placement, and orientation to cover arbitrarily located targets in an area of interest. We propose a computationally light-weight heuristic, where the number of used mobile cameras is close to those found by near-optimal algorithms. Specifically, we address this problem for non-uniform target distributions that naturally form clusters. Having light-weight heuristics will be particularly useful when the application is required to adapt to target mobility and or is implemented in embedded systems. Our simulation study shows that when clusters are sufficiently separated, the required number of cameras found by our proposed method is very close to those acquired by the near-optimal algorithm, whereas the computational complexity of our algorithm is about ten times less. We also deploy our algorithm on a drone testbed using off-the-shelf components to verify its feasibility. Cameras provide a rich source of information while being passive, cheap and lightweight for small Unmanned Aerial Vehicles (UAVs). In this work we present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. Two key contributions make this possible: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel pipeline via real world experiments of more than 2 kms through dense trees with an off-the-shelf quadrotor. Moreover our pipeline is designed to combine information from other modalities like stereo and lidar. This paper discusses the application of Unmanned Aerial Vehicles (UAV) for visual inspection and damage detection on civil structures. The quality of photos and videos taken by using such airborne vehicles is strongly influenced by numerous parameters such as lighting conditions, distance to the object and vehicle motion induced by environmental effects. Whilst such devices feature highly sophisticated sensors and control algorithms, specifically the effects of fluctuating wind speeds and directions affect the vehicle motion. The nature of vehicle movements during photo and video acquisition in turn affect the quality of the data and hence the degree to which damages can be identified. This paper discusses the properties of such flight systems, the factors influencing their movements and the resulting photo quality. Based on the processed data logged by the high precision sensors on the UAV the influences are studied and a method is shown by which the damage assessment quality may be quantified. This paper presents a solution to solve the car detection and counting problem in images acquired by means of unmanned aerial vehicles (UAVs). UAV images are characterized by a very high spatial resolution (order of few centimeters), and consequently by an extremely high level of details which calls for appropriate automatic analysis methods. The proposed method starts with a screening step of asphalted zones in order to restrict the areas where to detect cars and thus to reduce false alarms. Then, it performs a feature extraction process based on scalar invariant feature transform thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, it discriminates between keypoints assigned to cars and all the others, by means of a support vector machine classifier. The last step of our method is focused on the grouping of the keypoints belonging to the same car in order to get a “one keypoint-one car” relationship. Finally, the number of cars present in the scene is given by the number of final keypoints identified. The experimental results obtained on a real UAV scene characterized by a spatial resolution of 2 cm show that the proposed method exhibits a promising car counting accuracy. A concept for a multicopter unmanned aerial vehicle (UAV) automatically performing inspection flights at a wind turbine is proposed. Key aspects of the concept are (1) a priori 3D mapping of the plant and (2) spline-based flight path planning as well as (3) a collision avoidance and distance control system. A quadrotor UAV prototype and its dynamical model are presented. Validation of the different aspects is carried out in simulation and partially in indoor tests using Robot Operating System (ROS). Existence of a 3D map is an essential precondition for path planning and collision-free flight. A brief initial flight preceding the actual inspection with a 2D LiDAR sensor yields a point cloud of the plant which is used for 3D mapping. This map is efficiently generated and represented using octrees, a hierarchical data structure that can be used for 3D maps. Subsequently a smooth and collision-free flight path is generated using splines. For redundancy's sake navigation tasks not only rely on GPS but also on the LiDAR sensor mentioned before. The sensor allows for continuous estimation of the distance between multicopter and wind turbine. A distance control algorithm guarantees collision-free flight. This paper presents a framework for the automation of a small UAV using a low cost sensor suite, MNAV, and an embedded computing platform, Stargate, which together provide a complete avionics package for aerial robotic applications. In order to provide a complete INS solution (i.e., attitude, velocity, position, and biases), an extended Kalman filter algorithm is developed and implemented in real-time. A devised control strategy utilizes multiple PID loops with a hierarchy enabling simple attitude stabilization to full waypoint navigation. The developed ground station unit, a laptop computer, communicates with the avionics package via 802.11b WiFi, displays the aircraft critical information, provides in-flight PID gain tunings, and uploads waypoints through a simple GUI. The system is installed in an off-the-shelf delta-wing R C aircraft and demonstrates its performance for aerial robotic applications Search and exploration using multiple autonomous sensing platforms has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage or exploration problem, in that the target area needs to be continuously searched, minimizing the time between visitations to the same region. This difference does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. In this research we investigate techniques that are scalable, reliable, efficient, and robust to problem dynamics. These are tested in a multiple unmanned air vehicle (UAV) simulation environment, developed for this program. A semi-heuristic control policy for a single UAV is extended to the case of multiple UAVs using two methods. One is an extension of a reactive policy for a single UAV and the other involves allocation of sub-regions to individual UAVs for parallel exploration. An optimal assignment procedure (based on auction algorithms) has also been developed for this purpose. A comparison is made between the two approaches and a simplified optimal result. The reactive policy is found to exhibit an interesting emergent behavior as the number of UAVs becomes large. The control policy derived for a single UAV is modified to account for actual aircraft dynamics (a 3 degree-of-freedom nonlinear dynamics simulation is used for this purpose) and improvements in performance are observed. Finally, we draw conclusions about the utility and efficiency of these techniques. We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions. Aerial robotics can be very useful to perform complex tasks in a distributed and cooperative fashion, such as localization of targets and search of point of interests (PoIs). In this work, we propose a distributed system of autonomous Unmanned Aerial Vehicles (UAVs), able to self-coordinate and cooperate in order to ensure both spatial and temporal coverage of specific time and spatial varying PoIs. In particular, we consider an UAVs system able to solve distributed dynamic scheduling problems, since each device is required to move towards a certain position in a certain time. We give a mathematical formulation of the problem as a multi-criteria optimization model, in which the total distances traveled by the UAVs (to be minimized), the customer satisfaction (to be maximized) and the number of used UAVs (to be minimized) are considered simultaneously. A dynamic variant of the basic optimization model, defined by considering the rolling horizon concept, is shown. We introduce a case study as an application scenario, where sport actions of a football match are filmed through a distributed UAVs system. The customer satisfaction and the traveled distance are used as performance parameters to evaluate the proposed approaches on the considered scenario.