A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

Shafiq, Muhammad; Ali, Zain Anwar; Israr, Amber; Alkhammash, Eman H.; Hadjouni, Myriam

doi:10.3390/drones6050104

Cited by 11 publications

(7 citation statements)

References 31 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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“…However, the height of cliffs exceeds 100 m and the UAV must fly in the plane above the cliff, resulting in the unavailability of considerable information about the cliff, especially its bottom. Many studies have shown that in complex cliff conditions, the use of orthorectified and oblique images can reduce 3D modeling errors and optimize the survey route (Ali et al, 2019;Shafiq et al, 2022). The reason is that the more complex the angle of capture, the better the reproduction (Nesbit and Hugenholtz, 2019;Kozmus Trajkovski et al, 2020).…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

“…Consequently, labor costs remain high and collecting relevant data remains time-consuming and labor-intensive. To meet the challenge of high-quality modeling (Burdziakowski, 2018), many state of the art in fields (Ali et al, 2019;Shafiq et al, 2022) use many technologies such as UAV and artificial intelligence for three-dimensional reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling

Yan

Zhou

et al. 2022

Front. Plant Sci.

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The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

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“…The mechanisms of swarm intelligence are regarding the environment, interactions, and activities of the individuals in a swarm. No direct communication takes among the individuals in a swarm [25]. They interact with each other through environmental alterations.…”

Section: Mechanism Of Simentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

Self Cite

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The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

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A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

Cited by 11 publications

References 31 publications

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

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

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling

Motion Planning of UAV Swarm: Recent Challenges and Approaches

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