Group Of Unmanned Aerial Vehicles Research Articles

Secure UAV-to-UAV Systems With Spatially Random UAVs

In this letter, we investigate the secrecy performance of an unmanned aerial vehicle (UAV)-to-UAV system, where a UAV acts as the source ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> ) transmitting information to a legitimate UAV receiver while a group of UAVs trying to eavesdrop the information delivery between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> and legitimate UAV receiver. The locations of the legitimate UAV receiver and the eavesdropping UAVs are randomly distributed in the coverage space of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> . We first characterize the statistical characteristics of the signal-to-noise ratio over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula> to the legitimate UAV receiver links; and then the closed-form analytical expressions for secrecy outage probability and the average secrecy capacity have also been derived accordingly. Finally, Monte-Carlo simulations are carried out to verify our proposed analytical models.

A Game Theory Approach for Joint Access Selection and Resource Allocation in UAV Assisted IoT Communication Networks

The growing popularity of Internet of Things (IoT) with the requirements of highly reliable and low latency has imposed huge challenges to current cellular networks. Using small aerial platforms like unmanned aerial vehicles (UAVs) to assist terrestrial base stations (BSs) is attractive, but it often challenged by the lack of UAV access selection and resource allocation algorithm to balance the network performance and service cost. In this paper, we study the UAV access selection and BS bandwidth allocation problems in a UAV assisted IoT communication network, where a hierarchical game framework is presented. The complicated interactions among UAVs and BSs as well as the cyclic dependency is studied by applying the Stackelberg game theory. Wherein, the access competition among groups of UAVs is formulated as a dynamic evolutionary game and solved by an evolutionary equilibrium. On the other hand, the problem of how much bandwidth should BSs allocate to the UAVs is modeled as a noncooperative game, where the existence and the uniqueness of Nash equilibrium is analyzed. Stochastic geometry tool is used to model the position distribution of network nodes and drive the payoff expressions by taking into account different network parameters. The analytical results for the proposed hierarchical game model and the corresponding solutions are evaluated via simulations, which verify both the validity of our analysis and the effectiveness of the proposed algorithms.

A New Single-Display Intelligent Adaptive Interface for Controlling a Group of UAVs

The increasing use of unmanned aerial vehicles (UAVs) or drones in different civil and military operations has attracted attention of many researchers and science communities. One of the most notable challenges in this field is supervising and controlling a group or a team of UAVs by a single user. Thereupon, we proposed a new intelligent adaptive interface (IAI) to overcome to this challenge. Our IAI not only is empowered by comprehensive IAI architecture but also has some notable features like: presenting single-display user interface for controlling UAV team, leveraging user cognitive model to deliver right information at the right time, supporting the user by system behavior explanation, guiding and helping the user to choose right decisions. Finally, we examined the IAI with contributing eleven volunteers and in three different scenarios. Obtained results have shown the power of the proposed IAI to reduce workload and to increase user's situation awareness level and as a result to promote mission completion percentage.

A Hybrid Communication Scheme for Efficient and Low-Cost Deployment of Future Flying Ad-Hoc Network (FANET)

In recent years, FANET-related research and development has doubled, due to the increased demands of unmanned aerial vehicles (UAVs) in both military and civilian operations. Equipped with more capabilities and unique characteristics, FANET is able to play a vital role in mission-critical applications. However, these distinctive features enforce a series of guidelines to be considered for its efficient deployment. Particularly, the use of FANET for on-time data communication services presents demanding challenges in terms of energy efficiency and quality of service (QoS). Proper use of communication architecture and wireless technology will assist to solve these challenges. Therefore, in this paper, we review different communication architectures, including the existing wireless technologies, in order to provide seamless wireless connectivity. Based on the discussions, we conclude that a multi-layer UAV ad-hoc network is the most suitable architecture for networking a group of heterogeneous UAVs, while Bluetooth 5 (802.15.1) is the most favored option because of its low-cost, low power consumption, and longer transmission range for FANET. However, 802.15.1 has the limitation of a lower data rate as compared to Wi-Fi (802.11). Therefore, we propose a hybrid wireless communication scheme so as to utilize the features of the high data transmission rate of 802.11 and the low-power consumption of 802.15.1. The proposed scheme significantly reduces communication cost and improves the network performance in terms of throughput and delay. Further, simulation results using the Optimized Network Engineering Tool (OPNET) further support the effectiveness of our proposed scheme.

Beam Management and Self-Healing for mmWave UAV Mesh Networks

The point-to-point and point-to-multipoint communication features in the IEEE 802.11ay make it possible for unmanned aerial vehicles (UAVs) to form a UAV mesh network (flyMesh). In an 802.11ay based millimeter-wave (mmWave) flyMesh, many challenges are brought by the collaborative interactions of UAVs and the beam misalignment of the directional communication links. This paper studies how to guarantee the robustness of the mmWave flyMesh with respect to beam management and network self-healing. First, to cope with the problem of beam misalignment between UAVs or between the UAV group leader and relay base station/satellite caused by the movement of UAV(s), we propose a fast beam tracking mechanism. Second, in order to solve the problem of link failures due to departures of some UAVs from the group or failures, the self-healing mechanism is proposed to find alternative links to restore the network capability. Finally, to deal with the dynamic group leader changes of UAVs due to mobility, we propose an efficient UAV group leader re-selection mechanism to reduce the overhead of UAV group management. Through performance analysis and simulation, we demonstrate that our proposed mechanisms can effectively address the aforementioned problems and challenges.

Area Coverage by a Group of UAVs using the Broadcast Control Framework

Abstract This paper proposes a scheme for distributing a group of Unmanned Aerial Vehicles (UAVs) in an environment using Broadcast Control (BC) algorithm. BC is a recent technique that uses stochastic optimization to control a multi-agent system for accomplishing a global task. BC’s variant, the Pseudo-perturbation based BC (PBC), uses model based predicted decision and shows faster convergence than BC. In this paper, we have modified the existing BC schemes by incorporating relevant constraints to consider physical limitations of the agents. The proposed scheme is applied to a group of UAVs having kinematic constraints for an area coverage task. Results from numerical simulation show that the performance of BC methods are maintained, and more importantly, the designed path constraints for the UAVs produces a less erratic, continuous and realistic travel path as the UAVs fly to reach their goal.

The Approach to Managing a Group of UAVs as a System with Distributed Parameters

The research is devoted to one of the promising directions in the field of group control of unmanned aerial vehicles (UAVs), which function jointly within dense groups in solving specific problems. A large group of densely located UAVs is considered as a system with distributed parameters that are functions of time and spatial coordinates. The state of such a system is described by partial differential equations. A method for describing and managing such systems is proposed, based on the provisions of mathematical physics and the theory of systems with distributed parameters. The relevance of the proposed study is due to the need to solve complex tasks of trajectory control, taking into account the provision of controllability and prevention of collisions in the group of numerous UAVs.

A Two-Step Environment-Learning-Based Method for Optimal UAV Deployment

Unmanned aerial vehicles (UAVs) can be used as low-altitude flight base stations to satisfy the coverage requirements of wireless users in various scenarios. In practical applications, since the transmitted power and energy resources of the UAVs are limited and the propagation environments are complicated and time-variant, it is challenging to control a group of UAVs to ensure coverage performance while preserving the connectivity and safety of the UAV networks. To this end, a two-step environment-learning-based method is proposed for the intelligent deployment of the UAVs. First, a machine learning algorithm is used to establish an accurate prediction model of the link qualities from the UAVs to the users under a specific scenario for the next step. Then, a modified deep deterministic policy gradient (DDPG) algorithm is employed to control the movements of the UAVs according to the predicted link qualities and to maximize the proportion of covered users. The prioritized experience replay mechanism is introduced to the standard DDPG algorithm to accelerate the deployment procedure. The coverage performance is analyzed in both the interference-free situation and the situation with co-channel interference. Simulation results have shown that the proposed method has a higher convergence speed than the standard DDPG method. Additionally, the proposed deployment method can achieve higher coverage performance and better adaptability to the dynamic environment than three commonly used methods, the random method, the K-means-based method, and the statistical-channel-model-based method.

Use of Unmanned Aerial Vehicles for Livestock Monitoring based on Streaming K-Means Clustering

Unmanned Aerial Vehicle (UAV) as a tool of farming has attracted the interests of an increasing number of researchers. In this paper, we study the problem of deploying a group of UAVs to track and monitor the livestock such as cattle and sheep in a pasture. We assume all targeted animals have been fitted with GPS collars, and the mobility of each targeted animal cannot be ignored. We further assume the number of UAVs is sufficient for covering the entire pasture, and we aim to find the optimal UAVs’ deployment for minimizing the average UAV-animal distance. We first introduce a procedure of performing sweep coverage by UAVs. By deploying UAVs to achieving sweep coverage for the entire pasture, the initial locations of all targeted animals can be acquired. Then, determine and update the UAVs’ deployment by streaming k-means clustering based on the initial locations and received updated locations from the GPS collars. We demonstrate that our solution can always yield a lower average UAV-animal distance compared with a standard K-Means clustering algorithm without considering targeted animals’ mobility.

Adaptive Hello Interval in FANET Routing Protocols for Green UAVs

With the recent advances in unmanned aerial vehicles (UAVs), the development of energy-efficient networking technology for mission-oriented multiple cooperative UAVs has become crucial. Routing in flying ad-hoc networks (FANETs) with UAVs is a challenging issue because of the high speed and sudden changes in direction of UAVs. Traditional routing protocols in FANETs periodically send hello messages for the establishment and maintenance of the routes. However, sending hello messages periodically after a fixed interval increases bandwidth wastage when the hello interval is excessively short or causes long delays in neighbour discovery when the hello interval is overly long. Moreover, several disconnected UAV groups have been observed in which the group members are connected among themselves but detached from the main network. By exchanging excessive hello messages inside the group, the UAVs maintain an unnecessary neighbourhood, causing wastage of energy. However, FANETs have certain advantages, such as knowledge about mission-related information. To solve the problem of unnecessary energy drain, we propose a novel adaptive hello interval scheme-energy efficient hello (EE-Hello)-based on available mission-related information, such as the volume of the allowed airspace, number of UAVs, UAV transmission range, and UAV speed. We present a method to decide the distance that a UAV needs to travel before sending a hello message. We also specify a technique to determine the number of UAVs necessary to achieve specific network requirements, such as packet delivery ratio or throughput, with the expenditure of minimum energy. We show that the proposed EE-Hello can save about 25% of the energy currently used, by suppressing unnecessary hello messages without degrading the overall network throughput.

Cooperative moving path following for multiple fixed-wing unmanned aerial vehicles with speed constraints

This paper is to address a cooperative moving path following (CMPF) problem, in which a fleet of fixed-wing unmanned aerial vehicles (UAVs) are required to converge to and follow a desired geometric moving path while satisfying prespecified speed and spatial constraints. A representative application of the CMPF problem is the challenging mission scenario where a group of UAVs are tasked to track a moving ground target. The proposed methodology is based on the insight that a vehicle can follow a given path only through attitude control, thus leaving its speed as an extra input to be used at the coordination level. To deal with moving path following (MPF) of a single UAV, a non-singular control law is derived to steer the vehicle along the desired moving path which avoids the singularity problem in the previous MPF strategy. For multi-UAV coordination, a pursuit strategy is employed with the introduction of a virtual leader. To account for speed constraints and collision avoidance, conditions are derived under which the combined MPF and multi-UAV coordination closed-loop system is asymptotically stable while speed and spatial constraints are satisfied. Further simulation has been performed to demonstrate the effectiveness of the proposed method.

A Decentralized Formation and Network Connectivity Tracking Controller for Multiple Unmanned Systems

In this brief, we present a novel decentralized controller for multiple unmanned aerial vehicles (UAVs) to fly in a symmetric formation surrounding a hostile mobile target while maintaining and controlling the network connectivity. The multi-UAV information exchange topology is considered as a time-varying function of interagent distances. We investigate the impact of controller parameters on UAV group behavior to maintain connectivity. We also explore the use of the proposed controller to track a desired connectivity profile along with steering UAVs in a target-centric formation. Simulation results validate the effectiveness of the proposed controller.

Free aviatsіyny complex for vir_shennya tasks biolog_chny zakhistu roslin

Modern unmanned aircraft complexes are widely used in agriculture and allow you to make decisions to eliminate or localize problems such as cost optimization of fertilizers and pesticides, prediction crop yields, planning of logistics solutions, improving the class of grain, etc. Also the agricultural drone are used for spreading – trichogramma. Trichogramma is a biological material - biological plant protection mean, and accordingly it’s - an environment friendly. The trichogramma protects a large number of cultures from various species of pests. Kiev LLC ABRIS with the participation of students of “Igor Sikorsky Kyiv Polytechnic Institute” design and develop an unmanned aerial vehicle (UAV) for release of a trichogramma Designed UAV is equipped with release electromechanical devise and modern navigation equipment, which allows to accurately and evenly spread the trichogram over the entire treated area. The application rate of the entomological mean - trichogram is 3 g / ha. This leads to the fact that the use of manned aircraft technology becomes inefficient due to the fact that the necessary spread of active substance is extremely small values. The existing approach with using of manned aviation can not provide low costs spreading. Also the usage of light and ultra light manned aircraft is not appropriate due to low speed and altitude of the flight. Moreover the manned flight in extremally dangerous conditions (low altitude and speed) demands significant high professional pilot skills. Therefore it is more efficient to use UAV’s for such kinds of aviation applications. The maintenance group of the FLIRT Iron UAV consists of three members and can process up to 1000 ha per day. To provide the proportional spreading of trihogramma the UAV also is equipped with special aeronautical software for flight mission planning. The application rate of trichogramma is about 50-120 thousand wasps per one hectare, it corresponds to a volume of 3-7 cm 3 . The team of our design bureau has developed and manufactured a device that can be used with any type of existing UAV for spreading trichogram in fully automatic mode. The characteristics and design process of UAV complex is described in full version of the article.

The conceptual view of unmanned aerial vehicle implementation as a mobile communication node of active data transmission network

PurposeThe purpose of this paper is to propose the basis for the unification of unmanned aerial vehicle (UAV) group control protocols for the fast deployment of communication network on territories unsuitable for stationary nodes placement.Design/methodology/approachThe paper proposes the application of active data (AD) conception in which the data exist in a form of executable code allowing data packets to control its own propagation through network. The implementation is illustrated for some scenarios of UAV data communication network deployment, i.e., transmission of the AD using navigation functions and dynamic reconfiguration of the nodes.FindingsThe conception of AD expands the range of possible UAV group operations due to on-the-fly adaptation abilities to changes in existing or forthcoming group behavior protocols. This allows the real-time change of data transmission formats, frequency ranges, modulation types, radio network topologies which, in turn, provides the ability to dynamically form the special data transmission networks from a general purpose device temporarily reconfiguring them for data transmission task between transmitter and receiver beyond radio visibility range.Practical implicationsThe paper includes use cases for some situation of UAV data communication network deployment.Originality/valueThe paper aims to expand the UAV group control principles by implementing by rapid adaptation to changes in existing or forthcoming group behavior protocols.

Static Sensitivity of the Aerial Load Transport by Two Rotocopter Unmanned Aerial Vehicles

Aerial load transport by Unmanned Aerial Vehicles (UAVs) can be performed either using a single UAV or using a group of cooperative UAVs. The latter option is assumed more reliable, yet more demanding regarding the control and required power, than the former one. This article theoretically evaluates power consumption for aerial load transport by the use of two UAVs and compares it with the power consumption in case of a single UAV. In all treated cases, we assume a stationary level flight. For the stated comparison the theoretical model for aerial load transport by two identical UAVs is formulated. Generic flight characteristics and UAVs characteristics are prescribed, and corresponding solutions of the model evaluated. Independent parameters of the model are masses of the UAVs and the load, as well as flight velocity. Variables in the model are vertical and horizontal distances between the UAVs. The emphasis is put on the available instantaneous power and sensitivity to occasional wind gusts. We extract intervals of the model parameters for which each of the two UAVs is less loaded than a UAV which solitary carries a load. The sensitivity to wind gusts is lesser in configurations in which one UAV carries most of the load while the other provides the additionally needed instantaneous power.

Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

A Distributed Control Approach to Formation Balancing and Maneuvering of Multiple Multirotor UAVs

In this paper, we propose and experimentally verify a distributed formation control algorithm for a group of multirotor unmanned aerial vehicles (UAVs). The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in two- and three-dimensional environments. The complete distributed control framework is implemented with the combination of a fast model predictive control method executed at 50 Hz on low-power computers onboard multirotor UAVs and validated via a series of hardware-in-the-loop simulations and real-robot experiments. The experiments are configured to study the control performance in various formation cases of arbitrary time-varying (e.g., expanding, shrinking, or moving) shapes. In the actual experiments, up to four multirotors have been implemented to form arbitrary triangular, rectangular, and circular shapes drawn by the operator via a human–robot interaction device. We also carry out hardware-in-the-loop simulations using up to six onboard computers to achieve spherical formations and a formation moving through obstacles.

P074: Comparison of unmanned aerial vehicle technology versus standard practice in triaging casualties by paramedic students in a mass casualty incident scenario

Introduction: The proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the way medical incident commanders respond to mass casualty incidents (MCI) in triaging victims. The aim of this study was to compare UAV technology to standard practice (SP) in triaging casualties at a MCI Methods: A randomized comparison study was conducted with forty paramedic students from the Holland College Paramedicine Program. Using a simulated motor vehicle collision with moulaged casualties, iterations of twenty students were used for both a day and a night trial. Students were randomized to an UAV or a SP group. After a brief narrative participants either entered the study environment or used UAV technology where total time to triage completion, green casualty evacuation, time on scene, triage order and accuracy was recorded Results: A statistical difference in the time to completing of 3.63 minutes (95% CI: 2.45, 4.85, p=0.002) during the day iteration and a difference of 3.49 minutes (95% CI: 2.08,6.06, p=0.002) for the night trial with UAV groups was noted. There was no difference found in time to green casualty evacuation, time on scene or triage order. One hundred percent accuracy was noted between both groups. Conclusion: This study demonstrated the feasibility of using an UAV at a MCI. A non clinical significant difference was noted in total time to completion between both groups. There was no increase in time on scene by using the UAV while demonstrating the feasibility of remotely triaging green casualties prior to first responder arrival.

Comparison of Unmanned Aerial Vehicle Technology Versus Standard Practice in Identification of Hazards at a Mass Casualty Incident Scenario by Primary Care Paramedic Students.

A prospective observational cohort study was conducted with 21 students randomized into UAV or SP group, based on a MVC with 7 hazards. The UAV group remained at the UAV ground station while the SP group approached the scene. After identifying hazards the time and order was recorded. The mean time (SD, range) to identify the hazards were 3 minutes 41 seconds (1 minute 37 seconds, 1 minute 48 seconds-6 minutes 51 seconds) and 2 minutes 43 seconds (55 seconds, 1 minute 43 seconds-4 minutes 38 seconds) in UAV and SP groups corresponding to a mean difference of 58 seconds (P=0.11). A non-parametric permutation test showed a significant (P=0.04) difference in identification order. Both groups had 100% accuracy in hazard identification with no statistical difference in time for hazard identification. A difference was found in the identification order of hazards. (Disaster Med Public Health Preparedness. 2018;12:631-634).

Connectivity properties for UAVs networks in wireless ultraviolet communication

The ultraviolet (UV) scattering communication can be applied in military networked on-the-move and unattended ground sensor networks. This paper focuses on the connectivity properties of unmanned aerial vehicles (UAVs) network based on UV communication that ensures the secret communications between UAVs. UAVs network is consisting of a group of UAVs, each of which moving according to a particular mobility model. We discuss random waypoint (RWP) mobility model and circle movement based model (CMBM), which can describe the actual movement of UAVs, respectively. In this paper, the approximations of the probability that the network is k-connected are provided with consideration of transmission using on–off keying and pulse position modulation. More precisely, we evaluate the effects of node density, transmission power, and data rate on k-connectivity. The numerical examples show that the mobility degrades the connectivity probability. When the numbers of nodes $$n=500$$ and data rate $$R_b =10\,\hbox {kbps}$$ , the required transmission power for nodes moving according to RWP is lower than CMBM in order to achieve 2-connectivity of the UAVs network, but it is about twice that for uniformly distributed nodes.