Cooperative Unmanned Aerial Vehicles Research Articles

Joint Position and Resource Optimization for Multi-UAV-Aided Relaying Systems

Unmanned aerial vehicles (UAVs) can enhance the overall performance of terrestrial communication systems due to their high possibility of the line of sight (LoS) links and on-demand deployments. The advantages of the UAV position optimization now attract attentions from researchers to study the cooperation among static UAV relays. In this paper, we study a relaying system, where multiple UAVs establish a cooperative UAV relay network to help some transmitters (Txs) communicate with their corresponding receivers (Rxs) by employing orthogonal frequency division multiple access (OFDMA). We maximize the paired Tx-Rxs' minimum rate through solving a non-convex, information causality constraints involved problem by optimizing the UAV relays' locations, nodes' powers, and bandwidth allocations together. An iterative algorithm is proposed by using block coordinate descent (BCD) and successive convex approximation (SCA) methods. Moreover, we also propose a new position initialization method, discuss some special cases of our problem to show some insights for future works, and prove the convergence of our algorithm. Simulations show the effectiveness of our proposed algorithm, some interesting trade-offs about the optimized UAVs' locations, and the influence of available resources.

Joint Location and Beamforming Design for Cooperative UAVs With Limited Storage Capacity

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

Physical Layer Security in UAV Systems: Challenges and Opportunities

Unmanned aerial vehicle (UAV) wireless communications has experienced an upsurge of interest in both military and civilian applications, due to its high mobility, low cost, on-demand deployment, and inherent line-of-sight air-to-ground channels. However, these benefits also make UAV wireless communication systems vulnerable to malicious eavesdropping attacks. In this article, we aim to examine the physical layer security issues in UAV systems. In particular, passive and active eavesdropping are two primary types of attack in UAV systems. We provide an overview on emerging techniques, such as trajectory design, resource allocation, and cooperative UAVs, to fight against both types of eavesdropping in UAV wireless communication systems. Moreover, the applications of non-orthogonal multiple access, multiple-input multiple-output, and millimeter-wave in UAV systems are also proposed to improve the system spectral efficiency and to guarantee security simultaneously. Finally, we discuss some potential research directions and challenges in terms of physical layer security in UAV systems.

Opportunities and Challenges of Unmanned Aircraft Vehicles Networking in Confrontation Environment: Collaborative and Reliable Communication with Intelligence

With a wide range of applications of artificial intelligence (AI) technology in communication networks, collaborative and reliable communication with intelligence has enormous potential to improve the performance of unmanned aerial vehicles (UAVs) in complex confrontation environment. Two typical application scenarios of UAVs, assisted communication and swarm communication, are introduced after discussions of characteristics. In details, the preliminary network architecture is given to improve the adaptability in different scenarios. Then, combined with the characteristics of the UAVs and the anti-UAV countermeasures, the opportunities and challenges in UAVs cooperative communication are analyzed. Finally, highlighting the key design considerations of the AI technology gives possible solutions correspondingly, which deserves to be exploited for the cooperative and reliable UAVs’ communication.

Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control.

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

Test analysis of a scalable UAV conflict management framework

This study elaborates the conflict management framework of unmanned aerial vehicles, focusing on the identification of the spatiotemporal interdependencies between them, with consideration of the future scalability problems in highly dense traffic scenarios. The paper first tries to justify the applied separation criteria among small cooperative unmanned aerial vehicles based on their performance characteristics and the planned missions’ type. The adopted criteria, obtained from the simulations of 160 missions, present a testing asset, referring to a current lack of the spatiotemporal requirements and a need for extending the research in this area to provide a more rapid integration of these vehicles into the civil controlled airspace. The paper then elaborates the computational framework for the conflict detection and resolution function and operational metrics for causal identification of the spatiotemporal interdependencies between two or more cooperative vehicles. The vehicles are considered as a conflict mission system that strives to achieve an efficient solution by applying certain maneuvering measures, before a loss of separation occurs. The operational trials of five local, short-range missions, supported by the simulation scenario, demonstrate the potential for a time-based complexity analysis in the conflict resolution processes with less demanding and more efficient coordinated maneuvers. The results show that those maneuvers would not induce any new conflicts and disrupt the cooperative mission system when the spatial capacity only might not be favorable in provision of the avoidance maneuvers within an available airspace.

Drones Chasing Drones: Reinforcement Learning and Deep Search Area Proposal

Unmanned aerial vehicles (UAVs) are very popular and increasingly used in different applications. Today, the use of multiple UAVs and UAV swarms are attracting more interest from the research community, leading to the exploration of topics such as UAV cooperation, multi-drone autonomous navigation, etc. In this work, we propose two approaches for UAV pursuit-evasion. The first approach uses deep reinforcement learning to predict the actions to apply to the follower UAV to keep track of the target UAV. The second approach uses a deep object detector and a search area proposal (SAP) to predict the position of the target UAV in the next frame for tracking purposes. The two approaches are promising and lead to a higher tracking accuracy with an intersection over union (IoU) above the selected threshold. We also show that the deep SAP-based approach improves the detection of distant objects that cover small areas in the image. The efficiency of the proposed algorithms is demonstrated in outdoor tracking scenarios using real UAVs.

Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster.

Medical assistance is crucial to disaster management. In particular, the situation of survivors as well as the environmental information after disasters should be collected and sent back to cloud/data centers immediately for further interpretation and analysis. Recently, unmanned aerial vehicle (UAV)-aided disaster management has been considered a promising approach to enhance the efficiency of searching and rescuing survivors after a disaster, in which a group of UAVs collaborates to accomplish the search and rescue task. However, the battery capacity of UAVs is a critical shortcoming that limits their usage. Worse still, the unstable network connectivity of disaster sites could lead to high latency of data transmission from UAV to remote data centers, which could make significant challenges on real-time data collecting and processing. To solve the above problems, in this paper, we investigate an energy-efficient multihop data routing algorithm with the guarantee of quality-of-service for UAV-aided medical assistance. Specifically, we first study the data routing problem to minimize the energy consumption considering transmission rate, time delay, and life cycle of the UAV swarms. Then, we formulate the issue as a mixed-integer nonlinear programming model. Because of the Non-deterministic Polynomial-hardness of this problem, we propose a polynomial time algorithm based on a genetic algorithm to solve the problem. To achieve high efficiency, we further enhance our algorithm based on DBSCAN and adaptive techniques. Extensive experiments show that our approach can outperform the state-of-the-art methods.

Cooperative unmanned aerial vehicles with privacy preserving deep vision for real-time object identification and tracking

Human tracking is an important challenge in a wide variety of applications, including but not limited to, surveillance, military operations, and disaster relief services. Unmanned Aerial Vehicles (UAVs) allow the surveying of dangerous or impassable areas from a safe distance. They also provide a machine-based capability, which may not only solve resource constraint issues, but can also improve effectiveness and efficiency in the tracking task. The effectiveness of tracking is directly related to the angle of view and degree of freedom of the camera system. In this paper, we introduce a decentralized, distributed deep learning algorithm for Real-Time Privacy-preserving Target Tracking Re-Identification (RPTT-ReID) used by cooperative UAVs in complex and adversarial environments involving motion, crowded scenes, and varied camera angles. The efficiency of RPTT-ReID makes it amenable to edge computing applications. The proposed algorithmic approach resolves shortfalls with current tracking algorithms, specifically challenges in maintaining tracking when subjects cross paths, switch identity, or are occluded in a frame of view. We demonstrate the power of our approach both in single and multi-UAV scenarios to track movable targets by extracting the facial embedding information in crowds, in order to ensure the privacy of individuals captured by the UAVs without compromising the capability for target re-identification. We validate RPTT-ReID on a challenging video dataset of crowded scenes. Our experimental evaluation shows that the proposed approach is capable of tracking and re-identifying people in crowds despite blended trajectories with minimum and maximum accuracy of 79.91 ± 0.2% and 93.27 ± 0.1% respectively. The proposed approach is 18% faster than previous methods for tracking in crowded urban environments.

A study of mobility models for UAV Communication Networks

The Unmanned aerial vehicle communication network (UAVCN) is a group or swarm of unmanned aerial vehicles which can be used for specific military and civilian applications without human intercession. This network faces the design problem which is based on network mobility. The frequent topology changes affect communication and collaboration among the UAVs (Unmanned aerial vehicles). To govern the movement pattern of UAVCN different mobility models needed to be studied in order to solve this communication issue. In this paper, mobility models are explored which provides the particular mobility pattern to resolve the problem of collaboration, communication and cooperation of UAVs. These models have been categorized into five groups and classified each group in detail. These mobility models provide the platform to understand and implement the unmanned aerial communication network for specific environment scenarios. The mobisim simulator tool is used to generate the mobility model s trajectories for different mobility models.

Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO

This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.

Profit-Driven Adaptive Moving Targets Search with UAV Swarms.

This paper presents a novel distributed algorithm for a moving targets search with a team of cooperative unmanned aerial vehicles (UAVs). UAVs sense targets using on-board sensors and the information can be shared with teammates within a communication range. Based on local and shared information, the UAV swarm tries to maximize its average observation rate on targets. Unlike traditional approaches that treat the impact from different sources separately, our framework characterizes the impact of moving targets and collaborating UAVs on the moving decision for each UAV with a unified metric called observation profit. Based on this metric, we develop a profit-driven adaptive moving targets search algorithm for a swarm of UAVs. The simulation results validate the effectiveness of our framework in terms of both observation rate and its adaptiveness.

Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-infrastructure (U2I) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal to noise ratio (SNR) for the U2I link uploads its collected data directly to the BS through U2I communication, while the UAV with a low SNR for the U2I link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2I and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. Simulation results show that the proposed ISASOA can upload 10\% more data than the greedy algorithm.

Cooperative Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance, and Reconnaissance Missions

In this paper, we investigate a novel two-echelon ground vehicle and its mounted unmanned aerial vehicle cooperative routing problem (2E-GUCRP) for intelligence, surveillance, and reconnaissance (ISR) missions. In a typical cooperative system with ground vehicle (GV) and unmanned aerial vehicle (UAV), the UAV is launched from the GV, automatically flies to the designated target and return to the GV before powering off. Meanwhile, acting as a mobile platform, the GV can carry the UAV moving in a larger region and charge or exchange the UAV's battery. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A mixed integer programming (MIP) model, which could be solved by commercial software (e.g. CPLEX), is developed to formulate the problem. In order to quickly solve the medium-scale or above problems, two heuristics are designed. The random instances and practical case based on the road network in Changsha, China, are used for experiments. The computational results indicate that, compared to the mode of two-echelon vehicle routing problem (2E-VRP), the cooperative GV and UAV can complete the ISR missions in a much shorter time through utilizing the proposed 2E-GUCRP mode. Furthermore, discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.

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.

Trajectory Planning for Reconnaissance Mission Based on Fair-Energy UAVs Cooperation

Unmanned aerial vehicles (UAVs) have recently received growing popularity in reconnaissance missions due to their many advantages, such as high mobility, flexible deployment, and low operational costs. In this paper, we investigate how the UAVs should optimally exploit its mobility via trajectory planning to achieve the fairness of energy consumption with communication, hovering, and motion energy consumption in consideration. Most of the current works only consider motion energy consumption; however, communication and hovering energy consumption cannot be ignored. We first formulate this problem as a min-max tour cover problem that has been proved to be NP-hard. Then, a heuristic algorithm is proposed to minimize the maximum energy consumption of the reconnaissance UAVs by planning the trajectories. Next, to guarantee the fairness of energy consumption under scenarios with strict and firm energy requirements, we propose an approximation algorithm that can achieve an approximation ratio of 2.5. Finally, the extensive simulations are conducted under different settings to evaluate the performance of our proposed algorithms. The results show that the algorithms can improve the fairness of energy consumption and reduce the maximum energy consumption compared with other algorithms.

Analysis and Optimization of Multiple Unmanned Aerial Vehicle-Assisted Communications in Post-Disaster Areas

As a vital component of disaster response and relief, a wireless network needs to be rapidly deployed after a disaster strikes. Due to the advantages of large area coverage, low capital cost, and fast deployment, an unmanned aerial vehicle (UAV) is believed to be a potentially promising choice to recover wireless communication in post-disaster environments. In this paper, the performance gains of utilizing two cooperative UAVs for downlink transmission over a large number of emergency response rescue vehicles on the ground in post-disaster areas are explored. Toward this end, the concept of average channel access delay for a generic vehicle to establish a full transmission to an UAV is introduced, i.e., data packets are said to be successfully transmitted from a UAV to a vehicle only if the time duration for the vehicle covered by the UAV is greater than the specified average channel access delay. Based on the proposed concept, a stochastic geometry based mathematical framework to analyze the coverage probability and average achievable rate for a multi-UAV-assisted downlink network, where vehicles connect to the Internet via satellites in a two-hop manner, is presented. According to the derived closed-form solutions for the network performance metrics, extensive numerical results are provided to illustrate the network performance gains brought by UAVs. Additionally, optimal settings are also presented for network designers to efficiently determine the optimal network parameters so as to achieve the optimum network performances in post-disaster areas.

Posterior Cramér Rao Bounds for Cooperative Localization in Low-Cost UAV Swarms

Cooperative networks of unmanned aerial vehicles (UAV) offer interesting advantages such as increased efficiency and improved accuracy when compared with independently operating UAVs in most applications like remote sensing, mapping, surveillance, exploration, search and rescue, situational awareness, disaster management. However, the quality of the products derived using UAV data is very much dependent on the accuracy with which a UAV can be localized. Although cooperative localization has been shown to improve the localization accuracy of all the UAVs in a network even in global navigation satellite system (GNSS) challenging environments, not all UAVs in a network can achieve equal navigational performance. The objective of this paper is to analyze the various parameters that affect the performance of UAVs in a cooperative network. This paper derives the theoretical performance bound of the localization accuracy that can be achieved by any UAV in the network. This performance bound is derived using posterior Cramer Rao bound and is further used to analyze the effects of various parameters such as network geometry and connectivity, quality of available measurements and the availability of GNSS in the network. Through this analysis, the limitations and the benefits of a cooperative UAV swarm for any application (such as mapping or remote sensing) are presented.

Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency.

Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to set up a Flying Ad Hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapid deployable systems. The user experience on watching videos transmitted over FANETs should always be satisfactory even under influence of topology changes caused by the energy consumption of UAVs. In addition, the FANET must keep the UAVs cooperating as much as possible during a mission. However, one of the main challenges in FANET is how to mitigate the impact of limited energy resources of UAVs on the FANET operation in order to monitor the environment for a long period of time. In this sense, UAV replacement is required in order to avoid the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. In addition, decision-making must take into account energy consumption associated with UAV movements, since they are generally quite energy-intensive. This article proposes a cooperative UAV scheme for enhancing video transmission and global energy efficiency called VOEI. The main goal of VOEI is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an Software Defined Network (SDN) paradigm, the VOEI assumes the existence of a centrailized controller node to compute reliable and energy-efficiency routes, as well as detects the appropriate moment for UAV replacement by considering global FANET context information to provide energy-efficiency operations. Based on simulation results, we conclude that VOEI can effectively mitigate the energy challenges of FANET, since it provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to state-of-the-art algorithm. In addition, VOEI delivers videos with suitable Quality of Experience (QoE) to end-users at any time, which is not achieved by the state-of-the-art algorithm.