Fleet Of UAVs Research Articles

Analysis of multi-UAV communication networking problem in constrained geographic environments based on improved simulated annealing optimization algorithm

This paper conducts an in-depth exploration of UAV communication networking and path planning within geographically constrained environments. The study begins with necessary data preprocessing, which includes the conversion of latitude and longitude coordinates and the organization of antenna parameters. Through the development of a model based on coordinate geometry, this research effectively identifies communication blind spots and establishes a comprehensive geometric envelope for signal coverage. To enhance network efficiency and path planning, the paper introduces a hybrid optimization algorithm that combines simulated annealing and genetic algorithms. Simulated annealing is used to optimize the selection of communication types between network nodes, while genetic algorithms refine the UAV fleet's flight routes based on these communication strategies. This dual-layered approach allows for fine-tuned adjustments in response to dynamic environmental constraints. The effectiveness of this method is demonstrated through simulations, which reveal a communication coverage rate of 73.7272%. These results confirm that the proposed hybrid optimization technique significantly improves both communication coverage and operational efficiency of UAV networks in complex environments. This provides substantial theoretical insights and practical contributions to the field of UAV network optimization, which is particularly valuable for applications in remote or urban areas where geographical constraints pose significant challenges.

Path-Following Formation of Fixed-Wing UAVs under Communication Delay: A Vector Field Approach

In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test.

Autonomous data detection and inspection with a fleet of UAVs

Consider an area of interest A, where a set of nsites lie. Two kinds of information can be captured from each site: light and heavy information. A fleet of m homogeneous UAVs, each one equipped with a battery B, is available at a common depot, where the flight mission of each UAV starts and finishes. The problem we consider focuses on a single flight of the fleet of UAVs and aims at collecting their light information from all sites (that can be retrieved, not necessarily passing over each site, but simply “close” to it). At the same time, the fleet will have to select a limited number of sites from which to collect their heavy information. Flying among sites and acquiring information from them (both light and heavy) has a battery cost. On the other hand, a profit is associated with the action of acquiring heavy information from a site. We refer to the extraction of light and heavy information from a site as to weakly or strongly cover the site. The aim of the problem consists of retrieving light information from all sites while maximizing the overall profit, keeping the battery consumption of each UAV within B. In this paper, we model this real-life situation as a new combinatorial optimization problem that we call m3DIP, for which we provide a mixed integer programming model. Given the high degree of complexity of the problem, in this way we are not able to provide a solution in a reasonable time. To address larger instances we propose a matheuristic in which we exploit a path-based algorithm filled with only a subset of feasible cycles (paths) provided by different heuristics. The output indicates which path to select and the set of nodes to be strongly and weakly covered by each trip. We compare our matheuristic with the results obtained by every single heuristic on a large set of instances, showing that the matheuristic strongly outperforms them. An interesting insight is that even paths provided by a heuristic with very bad performances can be useful if combined with paths provided by other heuristics and if the coverage decisions are reoptimized by the matheuristic. We also show the benefit of adding fictitious additional points that UAVs can visit to weakly cover a subset of sites, without actually visiting none of them.

Improved Algorithms for Co-Scheduling of Edge Analytics and Routes for UAV Fleet Missions

Improved Algorithms for Co-Scheduling of Edge Analytics and Routes for UAV Fleet Missions

Availability of UAV Fleet Evaluation Based on Multi-State System

Availability of UAV Fleet Evaluation Based on Multi-State System

Cybersecurity risk analysis of multifunctional UAV fleet systems: a conceptual model and IMECA-based technique

The subject of this study is to ensure the cybersecurity of systems of multifunctional UAV fleets (SMF UAV). The purpose of this study is to identify and analyze the risks associated with the cybersecurity of multi-functional UAV fleets, develop models of threats, vulnerabilities, and attacks, and conduct IMECA analysis of cyber-attacks. Tasks: 1) analyze threats that may affect the security of multifunctional UAV fleets; 2) identify system vulnerabilities and their possible consequences in case of exploitation; 3) develop models of the system infrastructure and threats, vulnerabilities, and attacks, considering the specifics of the functionality and communication between system elements; 4) perform a risk-based analysis, identifying and classifying potential threats and their impact. The following results were obtained. The following results were obtained. 1. Cybersecurity threats to multifunctional UAV fleets are described and classified. 2. Identified and analyzed system vulnerabilities and their potential consequences. 3. Developed models of threats, vulnerabilities, and cyberattacks, considering the specifics of the UAV fleet. 4. Conducted a risk-based analysis, determined the level of threat, and developed recommendations for improving the cybersecurity of the UAV fleet based on the results of the IMECA analysis. Conclusions. The research emphasizes the importance of the developed model and tool for the detection and analysis of cyber threats to the SMF UAV. This allows increasing the cybersecurity and reliability of the system and ensuring timely response to cyber threats. Areas for further research: development of a model and method to consider the specifics of cyber threats and the technological characteristics of the SMF infrastructure; development and implementation of proactive protection tools in the context of combined cyber-attacks; and expansion of the scope of these tools in various industries, including smart cities.

Localization of Partially Hidden Moving Targets Using a Fleet of UAVs via Bounded-Error Estimation

Localization of Partially Hidden Moving Targets Using a Fleet of UAVs via Bounded-Error Estimation

Unmanned aerial vehicle passive positioning technology based on cross-location and target splitting

In order to ensure that the UAV fleet can maintain maximum electromagnetic silence and minimise the emission of UAV electromagnetic signals during formation flight, a theoretical method of pure azimuth passive positioning can be used to optimise their relative positions. This paper analyses different formation situations of UAVs and presents the best solution for pure azimuth passive positioning of UAVs in formation flight and the corresponding positions. The paper first establishes polar coordinates based on known orientation information. It uses the sine theorem for triangles in the plane and the joint cubic equations to obtain the final result. It then uses triangulation to verify the accuracy of the specific location of the UAV by solving a least squares problem. Using the idea of cross-location, two intersecting trajectories are constructed and their intersection is used to improve the specific position of the UAV. Due to the bias in the position of the location of the signal sent by the UAV, this paper uses of the idea of target segmentation to split the target into multiple sub-targets and implement them separately. This is done in order to avoid the need for more UAVs to send their signal while adjusting the UAV position, making the Drones evenly spaced around a circumference.

Multi-Layer 5G Network Slicing with UAVs: An Optimization Model

In this paper, we present a network-based optimization model describing a closed-loop supply chain for the provision of 5G network slices on demand to users and devices on the ground. The three-tier supply chain network consists of a fleet of pre-existing UAVs, to which others can be added, managed by a fleet of UAV controllers, whose purpose is to perform services requested by users and devices on the ground. The aim of this paper is to provide a constrained optimization problem through which the providers’ profits are maximized, determining the global optimal distributions of request flows, the global optimal distributions of executed services and the optimal reliability level of pre-existing UAVs of the fleet. We also derive the associated Variational inequality formulation of the problem and, finally, a numerical simulation is performed to validate the effectiveness of the model.

Robotic-biological systems for detection and identification of explosive ordnance: concept, general structure, and models

The subject of this study is systems for detection and identification (D&I) of explosive ordnance (EO). The aim of this study is to develop a concept, general structure, and models of a robotic-biological system for D&I of EO (RBS-D&I). The objectives are as follows: 1) to classify mobile systems for D&I of EO and suggest a concept of RBS-D&I; 2) to develop the general structure of RBS-D&I consisting of robotic (flying and ground) and biological subsystems; 3) to develop models of RBS-D&I including automaton, hierarchical, and operational ones; 4) to describe tasks and planned results of the article-related scientific project; and 5) to discuss research results. The following results were obtained. 1) The general structure of the RBS-D&I. The structure comprises the following levels: control and processing centres (mobile ground control and processing centre (MGCPC) and virtual control and processing centre); forces for detection and identification (fleet of unmanned aerial vehicles (FoU), biological detection information subsystem (BDIS), and robotic detection information subsystem (RDIS)); interference; natural covers and a bedding surface; and target objects (all munitions containing explosives, nuclear fission or fusion materials and biological and chemical agents). 2) A concept of RBS-D&I. The concept is based on RBS-D&I description, analysis, development, and operation as an integrated complex cyber-physical and cyber-biological system running in changing physical and information environments. 3) The RBS-D&I automata model. The model describes RBS-D&I operating in two modes. In mode 1, FoU and BDIS operate separately and interact through the MGCPC only. In mode 2, depending on the specifics of the tasks performed, FoU and RDIS can directly interact among themselves or through the MGCPC. 4) hierarchical model. The model has two sets of vertices: EO detection and platforms equipped with the necessary sensors. 5) An operational cycle model. The model describes land release operations via a methodology of functional modeling and graphic description of IDEF0 processes. Conclusions. The proposed concept and RBS-D&I solutions can provide high-performance and guaranteed EO detection in designated areas by the implementation of an intelligent platform and tools for planning the use of multifunctional fleets of UAVs and other RBS-D&I subsystems.

Fleet Design Optimization of Package Delivery Unmanned Aerial Vehicles Considering Operations

The conceptual design process of aircraft starts by deciding the representative mission requirements, followed by optimization of design variables to satisfy the given requirements. However, the appropriate mission requirements are not obvious, especially when designing package delivery unmanned aerial vehicles (UAVs; also called drones). The UAVs must accommodate various combinations of package weights and delivery distances. The complexity increases further when designing a heterogeneous fleet of UAVs that serves a large number of customers. This work addresses this problem by solving coupled design–operation optimization to find optimal mission requirements and optimal UAV designs simultaneously. We formulate this problem as a mixed-integer nonlinear optimization and propose a sequential heuristic algorithm to solve the coupled problem. The benchmark study of the proposed algorithm against a nonconvex branch-and-cut solver shows that the sequential heuristics are effective. We also demonstrate that the simultaneous UAV design and routing optimization reduces the UAV weight across the fleet by more than 12% on average compared to the conventional baseline.

Route Coordination of UAV Fleet to Track a Ground Moving Target in Search and Lock (SAL) Task Over Urban Airspace

Drone has become more and more popular in various civil applications due to the open of the low-altitude airspace and its easy operation. Unlike the common search and track task for the target, the new search and lock (SAL) task is focused on in this article. In this task, multiple drones first try to detect the moving ground target cooperatively. Then, they must lock the target by covering all the surrounding area of it at a low flight altitude, which indicates that the target can be watched clearly in all directions from then on. The SAL task can be applied in the panorama shot for the moving ground target. First, the low-altitude urban airspace is discretized into cubes, based on which the flight rules of drone are defined. The field of view (FOV) of drone is modeled considering the flight altitude and the block of buildings. For the cooperation among multiple drones, the constraints on the patten of waypoint, the communication distance, and the collision avoidance are all included. The goal in the search phase is to cover more area, which have not been visited recently to increase the probability of detecting the target, and it is expected to lock the target as soon as possible in the lock phase. A new swarm-based imitative learning optimization (SBILO) algorithm is proposed to determine the waypoint of drone in the search phase. To have a quick response to the escape behavior of the target in the lock phase, the waypoint of drone is generated in a distributed way to cover more surrounding area of the target and lock it gradually. The case of losing the target in the FOV of all drones is also addressed by covering more possible places where the target may appear. Simulation results demonstrate that the SAL task can be performed efficiently by the drones with the flight routes obtained by the proposed SBILO algorithm and the distributed asynchronous decision-make (DADM) approach.

Optimal waypoint assignment for designing drone light show formations

Advancements in technology and new drone policies have boosted the adoption of drones by various industries. With active research and development, ingenious applications using drones are being developed. One such innovative and creative application of swarm drones is the drone light show. Light shows are generally performed outdoors in a dedicated clear open air space, which attracts the usage of drones for the execution. A fleet of multiple UAVs illuminates the night sky with picturesque performances in a synchronized manner. Use of drones in light shows provides an eco-friendly and reusable alternative for unique ways of advertisements, celebrations, storytelling, and entertainment. To choreograph the performance of swarm drones means allocating waypoints to each drone for every formation and the complexity of designing increases with the number of drones. An intelligent optimal assignment system is much required to allocate waypoints to drones for each transition in order to create formations. We propose a static swarm-intelligence-based assignment approach named Constrained Hungarian Method for Swarm Drones Assignment (CHungSDA) for optimally assigning multi-UAVs to waypoints. This approach uses Hungarian algorithm as a base with added constraints specific to the targeted application. Several simulations of the proposed approach — carried out for different designs have shown promising results. The work will be of best reference to the UAV manufacturers for their functional design of UAVs and development of Ground Control Station Software to communicate with multiple UAVs, along with other companies that aim to create visual illustrations using UAVs.

Human–Machine Network Through Bio-Inspired Decentralized Swarm Intelligence and Heterogeneous Teaming in SAR Operations

Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–machine system that combines UAVs fast responsiveness with two robust, decentralized, and scalable bio-inspired techniques. Cloud-Sharing Network (CSN) and Pseudo-Central Network (PCN), based on Bacterial and Honeybee behaviors, are presented, and applied to Safe and Rescue (SAR) operations. A post-earthquake scenario is proposed, where a heterogeneous fleet of UAVs cooperates with human rescue teams to detect and locate victims distributed along the map. Monte Carlo simulations are carried out to test both approaches through state-of-the-art metrics. This paper introduces two hybrid and bio-inspired schemes to deal with critical scouting stages, poor communications environments and high uncertainly levels in disaster release operations. Role heterogeneity, path optimization and hive data-sharing structure give PCN an efficient performance as far as task allocation and communications are concerned. Cloud-sharing network gains strength when the allocated agents per victim and square meter is high, allowing fast data transmission. Potential applications of these algorithms are not only comprehended in SAR field, but also in surveillance, geophysical mapping, security and planetary exploration.

Modeling and Approximating the Visit of a Set of Sites With a Fleet of UAVs

Abstract In this paper, we consider the problem of flying over an area affected by a natural disaster (e.g. an earthquake) with a fleet of self-piloting unmanned aerial vehicles with cameras or other kinds of sensors on board; the aim is to acquire knowledge of the situation before rescuers start working. We model this situation as a new graph theoretical problem; then, we study its complexity providing an approximation ratio that becomes constant in some special (though practically reasonable) cases; finally, we put in relation the approximability of this new problem and of a well-known one. To the best of our knowledge, no previous work has ever considered all together the constraints we take into account from a theoretical point of view, so this is the first very general graph theoretical model for this problem.

On the Provision of Services With UAVs in Disaster Scenarios: A Two-Stage Stochastic Approach

In this paper, we propose a two-stage stochastic optimization model for the provision services in a multi-tiered network, consisting in user or devices on the ground requiring services to controller UAVs in flight. The requested services are executed by a fleet of pre-existing and additional UAVs. The possible occurrence of disaster scenarios and the related uncertainty and severity could cause an unexpected and sudden increase in demand. Hence, the aim of the proposed model is to optimize the management of the pre-existing and additional resources in order to maximize the total profit of service providers and, simultaneously, minimize the expected loss related to a possible unmet demand. A variational approach is proposed, and some numerical examples are performed to validate the effectiveness of our model.

Structured Flocking for Fixed Wing Multiple Agents

With developments in miniature UAVs, the problem of flocking has gained significant interest in both academia and industry, with applications in ISR, EW, and communication missions. This paper presents a flocking control scheme for a fleet of fixed-wing UAVs with constrained non-holonomic kinematics. The scheme is based on the flocking algorithms of Olfati-Saber and modified dynamic feedback linearization and demonstrates significant improvements in maneuverability and reliability, which are key for mission performance. The improvements in efficacy and reliability of the scheme are proved through simulations and a Monte Carlo study. With the proposed scheme, a flock of fixed-wing UAVs can reliably form and traverse any time-parameterized trajectory.

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations.

Multi-UAV routing for maximum surveillance data collection with idleness and latency constraints

This study discusses a multi-UAV surveillance routing problem that routes a UAV fleet from a base station to periodically capture data from sensing locations in a store-and-forward fashion during the planning horizon. Cooperated UAVs can wirelessly transfer data within communication range to gain the maximum collected data received at the base station while satisfying the idleness and latency constraints. An integer programming (IP) model is proposed on a time-space network to track the data movement and compared to an uncooperative data transport model. The results indicate that the proposed model transfers more data with less latency and idleness.

Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling

The presented problem concerns the route planning of a UAV fleet carrying out deliveries to spatially dispersed customers in a highly dynamic and unpredictable environment within a specified timeframe. The developed model allows for predictive (i.e., taking into account forecasted changing weather conditions) and reactive (i.e., enabling contingency UAVs rerouting) delivery mission planning (i.e., NP-hard problem) in terms of the constraint satisfaction problem. Due to the need to implement an emergency return of the UAV to the base or handling ad hoc ordered deliveries, sufficient conditions have been developed. Checking that these conditions are met allows cases to be eliminated if they do not guarantee acceptable solutions, thereby allowing the calculations to be sped up. The experiments carried out showed the usefulness of the proposed approach in DSS-based contingency planning of the UAVs’ mission performed in a dynamic environment.