A Distributed Deployment Strategy for a Network of Cooperative Autonomous Vehicles

Sharifi, Farid; Chamseddine, Abbas; Mahboubi, Hamid; Zhang, Youmin; Aghdam, Amir G.

doi:10.1109/tcst.2014.2341658

Cited by 49 publications

(18 citation statements)

References 20 publications

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“…In AV, the driving environment perception, cognition map, path planning and strategy control are the equivalent important task in AV [42][43][44]. How to drive like human beings is the most important task.…”

Section: Key Technologies In Avmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

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With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.

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Section: Key Technologies In Avmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

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“…More efficient forest fire monitoring, detection, and even fighting can be achieved when a fleet of multiple UAVs are deployed instead of a single UAV (Chao and Chen 2012;Sharifi et al 2014Sharifi et al , 2015a. However, this also requires that more practical algorithms for task assignment and cooperative control for multiple UAVs be developed.…”

Section: Cooperative Control Of Uavs In Forest Fire Monitoring Detecmentioning

confidence: 99%

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

2015

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Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.Résumé : Étant donné qu'ils sont rapidement manoeuvrables, qu'ils ont un grand rayon d'action opérationnel et qu'ils offrent une meilleure sécurité pour le personnel, les véhicules aériens sans pilote (UAV) équipés de systèmes de vision ont un potentiel énorme pour surveiller, détecter et combattre les feux de forêt. Au cours de la dernière décennie, la technologie de lutte contre les feux de forêt qui utilise des UAV s'est avérée de plus en plus prometteuse. Cet article présente un aperçu complet des progrès actuels dans ce domaine. D'abord, une brève revue du développement et de l'architecture des systèmes de surveillance, de détection et de combat des feux de forêt qui utilisent des UAV est présentée. Ensuite, les technologies reliées à la surveillance, à la détection et au combat des feux de forêt sont brièvement passées en revue, incluant celles qui sont associées à la détection, au diagnostic et au pronostic des feux, à l'élimination de la vibration des images et au contrôle coopératif des UAV. La dernière section décrit les défis actuels et les solutions potentielles liés à l'utilisation des UAV dans la lutte contre les feux de forêt. [Traduit par la Rédaction] Mots-clés : feu de forêt, surveillance, détection et combat des feux, traitement des images, télédétection, véhicules aériens sans pilote.

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“…To carry out such missions, an area coverage problem is often considered. Such problems have been vastly studied [9,26,30,32]. Some of the considered approaches [20,24] rely on set-theoretic methods [4], with different applications such as system deployment over a bounded region.…”

Section: Introductionmentioning

confidence: 99%

Decentralized MPC for UAVs Formation Deployment and Reconfiguration with Multiple Outgoing Agents

Chevet¹,

Vlad²,

Maniu³

et al. 2019

J Intell Robot Syst

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This paper presents a new decentralized algorithm for the deployment and reconfiguration of a multi-agent formation in a convex bounded polygonal area when considering several outgoing agents. The system is deployed over a twodimensional convex bounded area, each agent being driven by its own linear model predictive controller. At each time instant, the area is partitioned into Voronoi cells associated with each agent. Due to the movement of the agents, this partition is time-varying. The objective of the proposed algorithm is to drive the agents into a static configuration based on the Chebyshev center of each Voronoi cell. When some This work was supported by the CNRS' LIA on Information, Learning and Control and the Natural Sciences and Engineering Research Council of Canada.

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A Distributed Deployment Strategy for a Network of Cooperative Autonomous Vehicles

Cited by 49 publications

References 20 publications

Survey on Artificial Intelligence for Vehicles

Survey on Artificial Intelligence for Vehicles

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

Decentralized MPC for UAVs Formation Deployment and Reconfiguration with Multiple Outgoing Agents

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