Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture

Ali, Zain Anwar; Han, Zhangang; Masood, Rana Javed

doi:10.3390/s21113820

Cited by 37 publications

(21 citation statements)

References 38 publications

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“…They are also divided into two scenarios in terms of operation manner in the environment. In the first scenario, drones operate individually, while in the second scenario they fly in combination with others, which are normally known as a swarm of UAVs [32][33][34][35].…”

Section: State Of the Art Workmentioning

confidence: 99%

A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

Javan

Samadzadegan

Gholamshahi

et al. 2022

Drones

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The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening public safety. For this reason, accurate and rapid recognition of their types is very important to prevent their misuse and the security problems caused by unauthorized access to them. Performing this operation in visible images is always associated with challenges, such as the small size of the drone, confusion with birds, the presence of hidden areas, and crowded backgrounds. In this paper, a novel and accurate technique with a change in the YOLOv4 network is presented to recognize four types of drones (multirotors, fixed-wing, helicopters, and VTOLs) and to distinguish them from birds using a set of 26,000 visible images. In this network, more precise and detailed semantic features were extracted by changing the number of convolutional layers. The performance of the basic YOLOv4 network was also evaluated on the same dataset, and the proposed model performed better than the basic network in solving the challenges. Compared to the basic YOLOv4 network, the proposed model provides better performance in solving challenges. Additionally, it can perform automated vision-based recognition with a loss of 0.58 in the training phase and 83% F1-score, 83% accuracy, 83% mean Average Precision (mAP), and 84% Intersection over Union (IoU) in the testing phase. These results represent a slight improvement of 4% in these evaluation criteria over the YOLOv4 basic model.

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Section: State Of the Art Workmentioning

confidence: 99%

A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

Javan

Samadzadegan

Gholamshahi

et al. 2022

Drones

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“…Furthermore, among recent studies, Ali et al 34 proposed a self-organization, collective motion and control of a UAV swarm. They used Particle Swarm Optimization (PSO) and Multi-agent System (MAS) to design an algorithm towards self-synchronization among UAVs.…”

Section: State Of the Artmentioning

confidence: 99%

Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

Nagasawa¹,

Mas

Moya

et al. 2021

Sci Rep

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Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model.

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“…In order to control a collective behavior and conduct self-organization control of a swarm of 10 UAVs divided into two clusters of five agents, Z.A. Ali et al [ 5 ] proposed a hybrid meta-heuristic algorithm that merges the particle swarm optimization (PSO) with the multi-agent system (MAS). In the algorithm, PSO determines the best agents of a given cluster, and MAS chooses the best agent as the leader of each cluster.…”

Section: Summary Of the Special Issuementioning

confidence: 99%