Countering Large-Scale Drone Swarm Attack by Efficient Splitting

Chen, Wu; Meng, Xue; Liu, Jiajia; Guo, Hongzhi; Mao, Bomin

doi:10.1109/tvt.2022.3178821

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…Több cikk kétdimenziós környezeten alapuló útvonaltervezéssel foglalkozik, ami számunkra nem megfelelő a gyakorlati alkalmazásban, hiszen a mi rendszerünk térbeli útvonaltervezést igényel. A leltározáshoz alkalmas útvonaltervezés során leggyakrabban a következő algoritmusokat alkalmazzák a kutatók: a hagyományos kalmáralgoritmust (Chen-Dong, 2008), a genetikus algoritmust (Udvaros et al, 2019;Chen et al, 2022), a gyorsan táguló véletlen fa algoritmust (Wang-Yang, 2021), a hagyományos részecskeraj-optimalizációs algoritmust (PSO), a hangyaalgoritmust (Huang et al, 2022) és a neurális hálózatokat (Albattah et al, 2022). A kutatások többsége csak a legrövidebb repülési útvonalat vagy a legkisebb energiafogyasztást veszi figyelembe az egycélú optimalizálási modell létrehozásakor, és utána a konvex közelítési stratégiát alkalmazzák az útvonal meghatározására.…”

Section: Szakirodalmi áTtekintésunclassified

Automatikus raktárfeldolgozás autonóm drónokkal • Automated Warehouse Processing with Autonomous Drones

Gubán,

Udvaros

2023

Ma.Tud.

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Section: Szakirodalmi áTtekintésunclassified

Automatikus raktárfeldolgozás autonóm drónokkal • Automated Warehouse Processing with Autonomous Drones

Gubán,

Udvaros

2023

Ma.Tud.

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“…These two-dimensional solutions provide a suitable solution mostly for agricultural problems and patrol and monitoring tasks. The following algorithms are most often used by researchers during route planning suitable for inventory: the traditional squid algorithm [10]; the genetic algorithm [11,12]; the rapidly expanding random tree algorithm [13,14]; the traditional particle swarm optimization algorithm (PSO); the ant colony algorithm [15,16]; and deep neural networks [17,18]. Most of the research only uses the shortest flight path [19] or the least energy consumption [20] to generate the singleobjective optimization model, and further uses the convex approximation strategy [21] to calculate the route.…”

Section: Literature Reviewmentioning

confidence: 99%

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Gubán

Udvaros

2022

Drones

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In this paper, a mathematical model and solution for performing the inventory tasks of a multi-user, mixed warehouse in which neither satellite positioning nor other IT solutions can be used was presented. After reviewing the literature on road planning and the use of drones in warehouses, a method is presented that can be used to control drones that can be moved in all directions for imaging and transmission. The proposed method consists of three main steps. As a first step, we provide the mathematical model and solution method needed to determine the (optimal execution time) access routes required for processing the compartments of the warehouses. This is an initial step before starting the inventory. This considers the structure of the warehouse, its features, the number of drones, and the parameters of the drones. In the second step, based on the routes obtained in the first step, the real-time movement of the drones was controlled during processing, including camera movement and image recording. The third step is post-processing, i.e., processing the images for QR code identification, interpreting the QR code, and recognizing empty compartments for inventory control. A major advantage for users of the solution method is that the result can be achieved automatically without an external orientation device, relying solely on its own movement and the organization of a pre-planned route. The proposed model and solution method are suitable not only for inventory control, but also for solving other problems matching the model.

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“…In recent years, with the increase in mission complexity and the enhancement of UAV autonomy, unmanned aircraft swarm systems (UAS) have gradually received attention from researchers. Drone swarms have been used for target search and rescue [1,2], cluster operation [3,4], and target tracking [5,6]. Within the military domain, coordinated reconnaissance search attacks by UAV swarms in unknown environments is an important application.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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Countering Large-Scale Drone Swarm Attack by Efficient Splitting

Cited by 13 publications

References 42 publications

Automatikus raktárfeldolgozás autonóm drónokkal • Automated Warehouse Processing with Autonomous Drones

Automatikus raktárfeldolgozás autonóm drónokkal • Automated Warehouse Processing with Autonomous Drones

A Path Planning Model with a Genetic Algorithm for Stock Inventory Using a Swarm of Drones

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

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