UAV Fleet Mobility Model with Multiple Pheromones for Tracking Moving Observation Targets

Atten, Christophe; Channouf, Loubna; Danoy, Grégoire; Bouvry, Pascal

doi:10.1007/978-3-319-31204-0_22

Cited by 20 publications

(15 citation statements)

References 12 publications

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“…Swarms could provide a communication network, as is the case in the SMAVNET project (Hauert et al, 2009 , 2010 ). Teams or swarms of UAVs may be used to survey large areas (Basilico and Carpin, 2015 ; Atten et al, 2016 ). SWARM-BOT shows how smaller ground-based robots can work together to traverse challenging terrain (Mondada et al, 2004 ).…”

Section: Related Workmentioning

confidence: 99%

A Framework for Automatic Behavior Generation in Multi-Function Swarms

et al. 2020

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Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.

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Section: Related Workmentioning

confidence: 99%

A Framework for Automatic Behavior Generation in Multi-Function Swarms

et al. 2020

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“…In Atten et al, 25 group mobility model for special purpose was proposed. Nature-inspired mobility model, ant colony optimization (ACO), is employed to perform target discovery and tracking in an area by dropping repulsive pheromones on already scanned zones.…”

Section: Mobility Model For Aanet and Uwsnmentioning

confidence: 99%

A survey on network simulators in three-dimensional wireless ad hoc and sensor networks

Kang

Aldwairi

Kim

2016

International Journal of Distributed Sensor Networks

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As steady research in wireless ad hoc and sensor networks is going on, performance evaluation through relevant network simulator becomes indispensable procedure to demonstrate superiority to comparative schemes and suitability in most literatures. Thus, it is very important to establish credibility of simulation results by investigating merits and limitations of each simulator prior to selection. Based on this motivation, in this article, we present a comprehensive survey on current network simulators for new emerging research area, three-dimensional wireless ad hoc and sensor networks which is represented by airborne ad hoc networks and underwater sensor networks by reviewing major existing simulators as well as presenting their main features in several aspects. In addition, we address the outstanding mobility models which are main components in simulation study for self-organizing ad hoc networks. Finally, open research issues and research challenges are discussed and presented.

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“…Much recent research has focused on operating multiple UAVs in order to accomplish a given task efficiently, such as reconnaissance, target tracking, surveillance, etc. [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…One of important and common missions of multi-UAVs is the cooperative reconnaissance to search a target in a given open area. Thus, many studies have been conducted on efficient cooperative reconnaissance with multiple UAVs [3,4,[9][10][11][12]. The reconnoitering methods with autonomous flights are classified into the path planning method and the random mobility method [3].…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the random mobility method has no pre-planned paths for UAVs' movements. Each UAV decides its reconnaissance path dynamically based on random mobility models [3,4,[9][10][11][13][14][15][16]. This random mobility method can overcome unexpected events, such as UAV failure, and also provides unpredictability of reconnaissance to targets.…”

Section: Introductionmentioning

confidence: 99%

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A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone

Fathoni

Kim

2019

Applied Sciences

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Activities on Unmanned Aerial Vehicle (UAV) have increased over the last years and there are many fields in which UAVs can be used. One of the basic applications is reconnaissance of a given area using multiple UAVs. To perform reconnaissance mission, there are two methods: (i) path planning to navigate the pre-determined route; and (ii) random mobility method to explore without prior knowledge. In this paper, we indicate the imbalance problem of existing random mobility models for reconnaissance and propose a new model considering reconnaissance balance based on the number of visits. We divide the scanning area into N zones and then select a zone stochastically in which the search is insufficient. We evaluated the performance of the model by focusing on the coverage rate and average inter-visiting time. The proposed model shows that the 90%-coverage reaching time is improved by about 25% and the average inter-visiting time is improved by up to 15% compared to the previous approach.

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UAV Fleet Mobility Model with Multiple Pheromones for Tracking Moving Observation Targets

Cited by 20 publications

References 12 publications

A Framework for Automatic Behavior Generation in Multi-Function Swarms

A Framework for Automatic Behavior Generation in Multi-Function Swarms

A survey on network simulators in three-dimensional wireless ad hoc and sensor networks

A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone

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