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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Future Unmanned Aircraft Systems (UASs) are expected to be nearly autonomous and composed of heterogeneous Unmanned Aerial Vehicles (UAVs). While most of the current research focuses on UAV avionics and control algorithms, ground task automation has come to the attention of researchers during the past few years. Ground task automation not only relieves human operators, but may also expand the UAS operation area, improve system coverage and enable operation in risky environments without posing a threat to humans. We propose a model to evaluate the coverage of a given UAS. We also compare different solutions for various modules of an automated battery replacement system for UAVs. In addition, we propose a ground station capable of swapping a UAV's batteries, followed by a discussion of prototype components and tests of some of the prototype modules. The proposed platform is well-suited for high-coverage requirements and is capable of handling a heterogeneous UAV fleet. 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. 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. This paper is relating to the application of Integrated Vehicle Health Management (IVHM) concepts based on Prognostics and Health Monitoring (PHM) techniques to Multi-UAV systems. Considering UAV as a mission critical system, it is expected and required to accomplish its operational objectives with minimal unscheduled interruptions. So that, it does make sense for UAV to take advantage of those techniques as enablers for the readiness of multi-UAV. The main goal of this paper is to apply information from a PHM system to support decision making through an IVHM framework. PHM system information, in this case, comprises UAV remaining useful life (RUL) estimations. UAV RUL is computed by means of a fault tree analysis that it is fed by a distribution function from a probability density function relating time and failure probability for each UAV critical components. The IVHM framework, in this case, it is the task assignment based on UAV health condition (RUL information) using the Receding Horizon Task Assignment (RHTA) algorithm. The study case was developed considering a team of electrical small UAVs and pitch control system was chosen as the critical system. Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds. A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine). Small Unmanned Aerial Vehicles (UAVs) have been proposed for use in a variety of areas including hazard analysis, disaster monitoring, agricultural mapping, and so on. Currently, the development of flight control systems (FCS) for small UAVs is complicated, time-consuming and error-prone. To address these challenges, we present a reconfigurable flight control system architecture (RFCSA) for small UAVs. RFCSA allows rapidly integrating hardware modules and verifying control laws. It utilizes a modular-based framework, in which implementation details of a typical function are packaged into a function module. In addition, each function module in RFCSA is stand-alone with its own processors, memories, power conversion and communication interfaces. In this way, the system designer could be able to focus on the system implementation, rather than pay much attention to the design details of low-level functions. Moreover, an event-driven Service-Oriented Architectures (SOA) is proposed to minimize the coupling between modules and improve the performance of inter-module interactions. The paper is organized as follows: Firstly, the challenges and requirements for small UAVs' flight control systems are discussed. Secondly, the hardware architecture based on modular concept is developed. Thirdly, event driven SOA is proposed, and the mechanisms for modules to share information and coordinate activities are introduced. Finally, the conclusion and future work is pointed out. An Unmanned Aerial Vehicle (UAV) is an aircraft without onboard human pilot, which motion can be remotely and or autonomously controlled. Using multiple UAVs, i.e. a fleet, offers various advantages compared to the single UAV scenario, such as longer mission duration, bigger mission area or the load balancing of the mission payload. For collaboration purposes, it is assumed that the UAVs are equipped with ad hoc communication capabilities and thus form a special case of mobile ad hoc networks. However, the coordination of one or more fleets of UAVs, in order to fulfill collaborative missions, raises multiples issues in particular when UAVs are required to act in an autonomous fashion. Thus, we propose a decentralised and localised algorithm to control the mobility of the UAVs. This algorithm is designed to perform surveillance missions with network connectivity constraints, which are required in most practical use cases for security purposes as any UAV should be able to be contacted at any moment in case of an emergency. The connectivity is maintained via a tree-based overlay network, which root is the base station of the mission, and created by predicting the future positions of one-hop neighbours. This algorithm is compared to the state of the art contributions by introducing new quality metrics to quantify different aspect of the area coverage process (speed, exhaustivity and fairness). Numerical results obtained via simulations show that the maintenance of the connectivity has a slight negative impact on the coverage performances while the connectivity performances are significantly better. Unmanned Aerial Vehicles (UAVs) are increasingly demanded in civil, military and research purposes. However, they also possess serious threats to the society because faults in UAVs can lead to physical damage or even loss of life. While increasing their intelligence, for example, adding vision-based sense-and-avoid capability, has a potential to reduce the safety threats, increased software complexity and the need for higher computing performance create additional challenges -- software bugs and transient hardware faults -- that must be addressed to realize intelligent and safe UAV systems. In this paper, we present a fault tolerant system design for UAVs. Our proposal is to use two heterogeneous hardware and software platforms with distinct reliability and performance characteristics: High-Assurance (HA) and High-Performance (HP) platforms. The HA platform focuses on simplicity and verifiability in software and uses a simple and transient fault tolerant processor, while the HP platform focuses on intelligence and functionality in software and uses a complex and highperformance processor. During the normal operation, the HP platform is responsible for controlling the UAV. However, if it fails due to transient hardware faults or software bugs, the HA platform will take over until the HP platform recovers. We have implemented the proposed design on an actual UAV using a low-cost Arduino and a high-performance Tegra TK1 multicore platform. Our case-studies show that our design can improve safety without compromising performance and intelligence of the UAV. 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. Purpose – The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.Design methodology approach – HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on... *Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) – aircraft remotely operated from the ground – and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range. 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. Autonomous Unmanned Aerial Vehicles (UAVs) have gained popularity due to their many potential application fields. Alongside sophisticated sensors, UAVs can be equipped with communication adaptors aimed for inter-UAV communication. Inter-communication of UAVs to form a UAV swarm raises questions on how to manage its communication structure and mobility. In this paper, we consider therefore the problem of establishing an efficient swarm movement model and a network topology between a collection of UAVs, which are specifically deployed for the scenario of high-quality forest-mapping. Introduce a new time-dependent UAV heterogeneous fleet routing problem.Consider several objective functions and respect drones operational requirements.Design a MILP model in order to find sets of non-dominated solutions.Consider a model able to tackle multi-layer scenarios with package exchanging points.Integrate UAV into the new concepts of mini microgrid systems inside smart cities. This paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational requirements. A green routing problem is designed for overcoming difficulties that exist as a result of limited vehicle driving range. Due to the large amount of drones emerging in the society, UAVs use and efficiency should be optimized. In particular, these kinds of vehicles have been recently used for delivering and collecting products. Here, we design a new real-time routing problem, in which different types of drones can collect and deliver packages. These aerial vehicles are able to collect more than one deliverable at the same time if it fits their maximum capacity. Inspired by a multi-criteria view of real systems, seven different objective functions are considered and sought to be minimized using a Mixed-Integer Linear Programming (MILP) model solved by a matheuristic algorithm. The latter filters the non-dominated solutions from the pool of solutions found in the branch-and-bound optimization tree, using a black-box dynamic search algorithm. A case of study, considering a bi-layer scenario, is presented in order to validate the proposal, which showed to be able to provide good quality solutions for supporting decision making. We consider the design of vision-based control algorithms for unmanned aerial vehicles (UAVs), so as to enable a UAV to autonomously follow a person. A new vision-based control architecture is proposed with the goals of 1) robustly following the user and 2) implementing following behaviors programmed by manipulation of visual patterns. This is achieved within a detection tracking paradigm, where the target is a programmable badge worn by the user. This badge contains a visual pattern with two components. The first is fixed and used to locate the user. The second is variable and implements a code used to program the UAV behavior. A biologically inspired tracking recognition architecture, combining bottom-up and top-down saliency mechanisms, a novel image similarity measure, and an affine validation procedure, is proposed to detect the badge in the scene. The badge location is used by a control algorithm to adjust the UAV flight parameters so as to maintain the user in the center of the field of view. The detected badge is further analyzed to extract the visual code that commands the UAV behavior This is used to control the height and distance of the UAV relative to the user. Firstly, the characteristics of UAV visual tracking system were analysed, secondly the three-dimensional oculomotor control system model of biomimetic eye was developed based on the oculomotor neural paths. Then a new UAV visual tracking system was established. The on-board pan-tilt camera control system based on biomimetic eye can compensate the deflection caused by the UAV rotation and the movement of ground relative to the UAV. The pan-tilt camera can simulate human eye movements including smooth pursuit, VOR and OKR, and the control pattern of camera motor is velocity control, thus it can track the ground moving target continuously, smoothly, clearly and stably like human eye. Finally the experimental results were presented. A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches. A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution. 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.