UAV swarm control strategies: A case study for leak detection

Braga, Rafael Gomes; Silva, Roberto Carlos Lyra da; Ramos, Alexandre C. B.; Mora‐Camino, Félix

doi:10.1109/icar.2017.8023514

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…Other uses of Reynolds rules also include flocking navigation for drone groups in search of gas emitting sources (Braga et al, 2017). Although the Particle Swarm Optimization algorithm (Kennedy & Eberhart, 1995) is used to compute the velocity vector for one behavior, the values of other traditional rule weights still require to be empirically acquired.…”

Section: Related Workmentioning

confidence: 99%

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An adaptive weighting mechanism for Reynolds rules-based flocking control scheme

Hoang

Tran

et al. 2021

PeerJ Computer Science

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Cooperative navigation for fleets of robots conventionally adopts algorithms based on Reynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collisions. The analytical results of behavioral rules’ impact also validate the proposed weighting mechanism's effectiveness leading to improved performance.

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Section: Related Workmentioning

confidence: 99%

“…Besides, UAV swarms can take soldiers' place to sweep a large area in aerial reconnaissance missions (Wang et al, 2019). Since they are typically flown uninhabited, flocks of remotely piloted aircraft can be utilized in lethal situations such as gas leakage detection (Braga et al, 2017) or firefighting (Innocente & Grasso, 2019).…”

Section: Introductionmentioning

confidence: 99%

An adaptive weighting mechanism for Reynolds rules-based flocking control scheme

Hoang

Tran

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Additionally, for each particular scenario, the weight coefficients' values can only be found manually to achieve the best performance. Other uses of Reynolds rules also include flocking navigation for drone groups in search of gas emitting sources (Braga et al, 2017). Although the Particle Swarm Optimization algorithm (Kennedy and Eberhart, 1995) is used to compute the velocity vector for one behavior, the values of other traditional rule weights still require to be empirically acquired.…”

Section: Computer Sciencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "An adaptive weighting mechanism for Reynolds rules-based flocking control scheme (v0.1)"

2021

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Cooperative navigation for fleets of robots conventionally adopts algorithms based onReynolds's flocking rules, which usually use a weighted sum of vectors for calculating the velocity from behavioral velocity vectors with corresponding fixed weights. Although optimal values of the weighting coefficients giving good performance can be found through many experiments for each particular scenario, the overall performance could not be guaranteed due to unexpected conditions not covered in experiments. This paper proposes a novel control scheme for a swarm of Unmanned Aerial Vehicles (UAVs) that also employs the original Reynolds rules but adopts an adaptive weight allocation mechanism based on the current context than being fixed at the beginning. The simulation results show that our proposed scheme has better performance than the conventional Reynolds-based ones in terms of the flock compactness and the reduction in the number of crashed swarm members due to collision. The analytical results of behavioral rules' impact also validate the proposed weighting mechanism's effectiveness leading to improved performance.

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“…Braga vd. [14] sürü İHA'larda birlikte hareket kabiliyetini sağlayabilmek ve gaz sızıntısını tespit etmek için önerilen kontrol algoritması olarak parçacık sürü optimizasyonu algoritmasını kullanmışlardır. Sürü halinde hareketi ve çarpışmayı önlemek için gerekli olan kurallardan bahsedilmiş ve sistem bunlara uygun tasarlanmıştır.…”

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Sürü İnsansız Hava Araçlarının Görev Paylaşımı için Genetik Algoritma Tabanlı Bir Yaklaşım

KARAKÖSE

2022

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Öz: İnsansız Hava Aracı (İHA) öncelikle savunma ve kamusal alanlarda kullanılmaya başlamış daha sonra kullanım alanları giderek artmıştır. İHA'ların teknolojik gelişimleri ve otonom bir şekilde görevlerini gerçekleştirmeleri açısından büyük önem taşıyan optimizasyon çalışmaları, yapılan bilimsel uygulamalarda önemli bir yere sahiptir. Askeri ve diğer tüm kullanım alanlarında büyük etkileri olan İHA'ların görev planlamalarının, görev icraları sırasında ortaya çıkan yüksek maliyet nedeniyle verimli bir şekilde hazırlanması gerekmektedir. Bu çalışmada, İHA'ların hedeflerine görevlendirilirken farklı koşullara ve parametrelere göre hedef dağılımlarının en uygun şekilde yapılabilmesi için bir inceleme yapılmış ve genetik algoritma kullanılarak çözümler sunulmuştur. Birden çok İHA'nın hedeflere ulaşmak için izleyeceği yol, yoldaki engeller hesaba katılarak seçilmiştir. Aynı zamanda, hedeflerdeki düşman sayısına göre o hedefe kaç İHA'nın görevlendirileceğini belirlemek bu çalışmanın temel amaçlarından birisidir. Hedeflerde bulunan düşman sayısına göre görev dağılımı gerçekleştirilirken İHA'ların hedeflere olan uzaklığı da dikkate alınmıştır. Ek olarak, güzergâhı takip ederken düşmanlar ile karşılaşacak olan İHA'ların harcadıkları enerjinin en aza indirilmesi de hedeflenmiştir.

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UAV swarm control strategies: A case study for leak detection

Cited by 12 publications

References 13 publications

An adaptive weighting mechanism for Reynolds rules-based flocking control scheme

An adaptive weighting mechanism for Reynolds rules-based flocking control scheme

Peer Review #1 of "An adaptive weighting mechanism for Reynolds rules-based flocking control scheme (v0.1)"

Sürü İnsansız Hava Araçlarının Görev Paylaşımı için Genetik Algoritma Tabanlı Bir Yaklaşım

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