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Adapting Swarm Intelligence to a Fixed Wing Unmanned Combat Aerial Vehicle Platform

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Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications

Part of the book series: Studies in Big Data ((SBD,volume 132))

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Abstract

The majority of the swarm UAV studies focus on a single aspect, only investigating the stages such as formation development, path planning, or target tracking for a swarm currently in mission flight. Besides, the dynamic coordination and operation of the system based on the new commands that can be transmitted to the swarm during the mission are not taken into account; that is, the input of the ground resources is often ignored. In this study, all stages of a swarm of unmanned combat aerial vehicles (UCAV), from take-off to the end of the mission, are detailed in a single holistic framework, including communication with the ground station and intercommunication between swarm members. The designed solution is a platform that will enable the swarm structure to prevail by develo** alternative strategies and tactics against existing manned or unmanned air, land, and sea platforms. In this context, operational algorithms have been developed for fixed-wing, fully autonomous controlled UCAVs, which can successfully detect in-sight and beyond-sight targets for a desired period of time, and can communicate seamlessly with ground stations. Furthermore, dynamic swarm-type algorithms have been developed in order to fulfill the desired task in the event of the loss of any UCAV during the mission, to replace the lost vehicle with a new vehicle, and to communicate directly with the UCAVs in the swarm. As a result of adapting swarm intelligence to the UCAV platform, all individuals in the swarm perform tasks such as taking off in formation, adding or removing new individuals to the swarm, and formation protection. Moreover, they have the ability to change direction in a swarm, change formation, split or merge, navigate, ascend and descend, and simultaneous/sequential auto-landing as a swarm.

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Abbreviations

Backbone:

First layer of the YOLO algorithm.

CNN:

Convolutional Neural Network.

CSPResNext50:

Backbone architecture.

CSPDarknet53:

Backbone architecture.

CSRT:

Channel and Spatial Reliability Tracking.

EfficientNet-B3:

Backbone architecture.

Faster R-CNN:

Faster Region-Based Convolutional Neural Network.

FOV:

Field of View.

FPS:

Frame Per Second.

Head:

Third layer of the YOLO algorithm.

ImageNet:

Detection algorithm training dataset.

IVG:

Impact Vector Guidance.

KCF:

Kernelized Correlation Filter.

LVFG:

Lyapunov Vector Field Guidance.

Neck:

Second layer of the YOLO algorithm.

YOLO:

You Only Look Once.

OpenCV:

An open-source library for programming functions.

SSD:

Single Shot Detector.

TCP:

Transmission Control Protocol.

TLD:

Tracking-Learning-Detection.

UDP:

User Datagram Protocol.

UGV:

Unmanned Ground Vehicle.

VTOL:

Vertical Take-Off and Landing.

altitude:

Flight altitude (feet).

arrowhead_array:

Instantaneous position of the UCAV in the arrowhead formation.

d1:

The distance between the UCAV with the arrowhead_array value of 1 (UCAV-1) and the guide UCAV (feet).

d1x:

Parameter to reflect the distance of the guide UCAV and UCAV-1 to other UCAVs in 2D space (feet).

d1y:

Parameter to reflect the distance of the guide UCAV and UCAV-1 to other UCAVs in 2D space (feet).

d2:

Distance between consecutive UCAVs (feet).

def.ucav_com[‘ucav_formation’][‘type’]:

Flight formation type.

def.ucav_com[‘guide_ucav'][‘gps_noise_flag’]:

GPS service availability.

direction (dir):

The value used to determine which wing a UCAV is on in the formation.

dispatch:

Departure command value.

prism_array:

Instantaneous position of the UCAV in the prism formation.

theta (θ):

Angle made by UCAVs other than UCAV-1 in a prism formation with the direction of flight of the guide UCAV (degrees).

ucav_com:

Data set containing ucav_id and ucav_link.

ucav_id:

Position of a UCAV in a formation.

ucav_link:

Instant location and speed information of all UCAVs within the communication range of a default UCAV.

x:

X-coordinate of the guide UCAV (longitude).

x_vel:

X-component of the speed of guide UCAV (knots).

y:

Y-coordinate of the guide UCAV (latitude).

y_vel:

Y-component of the speed of guide UCAV (knots).

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  1. Unmanned/Intelligent Systems Lab, Faculty of Aeronautics and Astronautics, Tarsus University, Mersin, 33400, Turkey

    Murat Bakirci & Muhammed Mirac Ozer

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  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero, Tamaulipas, Mexico

    Laura Cruz-Reyes

  3. School of Engineering, Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Universidad Tecnológica de La Habana “José Antonio Echeverría”, La Habana, Cuba

    Alejandro Rosete

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Bakirci, M., Ozer, M.M. (2023). Adapting Swarm Intelligence to a Fixed Wing Unmanned Combat Aerial Vehicle Platform. In: Rivera, G., Cruz-Reyes, L., Dorronsoro, B., Rosete, A. (eds) Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications. Studies in Big Data, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-031-38325-0_18

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