Energy-Time Efficiency in Aerial Swarm Deployment

Stirling, Timothy; Floreano, Dario

doi:10.1007/978-3-642-32723-0_1

Cited by 15 publications

(16 citation statements)

References 21 publications

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“…Using the ideas presented in [14, 15] and [26, 29, 30] an UAV swarm can establish their own communications network. This network can be used for communicating swarm tasks or even to help other robots or a human team for subsequent work [12, 14, 15, 27, 31, 32].…”

Section: Related Workmentioning

confidence: 99%

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Modelling multi-rotor UAVs swarm deployment using virtual pheromones

et al. 2018

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In this work, a swarm behaviour for multi-rotor Unmanned Aerial Vehicles (UAVs) deployment will be presented. The main contribution of this behaviour is the use of a virtual device for quantitative sematectonic stigmergy providing more adaptable behaviours in complex environments. It is a fault tolerant highly robust behaviour that does not require prior information of the area to be covered, or to assume the existence of any kind of information signals (GPS, mobile communication networks …), taking into account the specific features of UAVs. This behaviour will be oriented towards emergency tasks. Their main goal will be to cover an area of the environment for later creating an ad-hoc communication network, that can be used to establish communications inside this zone. Although there are several papers on robotic deployment it is more difficult to find applications with UAV systems, mainly because of the existence of various problems that must be overcome including limitations in available sensory and on-board processing capabilities and low flight endurance. In addition, those behaviours designed for UAVs often have significant limitations on their ability to be used in real tasks, because they assume specific features, not easily applicable in a general way. Firstly, in this article the characteristics of the simulation environment will be presented. Secondly, a microscopic model for deployment and creation of ad-hoc networks, that implicitly includes stigmergy features, will be shown. Then, the overall swarm behaviour will be modeled, providing a macroscopic model of this behaviour. This model can accurately predict the number of agents needed to cover an area as well as the time required for the deployment process. An experimental analysis through simulation will be carried out in order to verify our models. In this analysis the influence of both the complexity of the environment and the stigmergy system will be discussed, given the data obtained in the simulation. In addition, the macroscopic and microscopic models will be compared verifying the number of predicted individuals for each state regarding the simulation.

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Section: Related Workmentioning

confidence: 99%

“…We want to underline that the probabilistic microscopic model and the stigmergy system that will presented here take into account the inherent uncertainty of the real robotic systems, which do not occur in other approaches that assume ideal worlds such as [26, 29, 30]. …”

Section: Related Workmentioning

confidence: 99%

Modelling multi-rotor UAVs swarm deployment using virtual pheromones

et al. 2018

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“…From an energetic point of view, we must take into account the difference between the consumption of a UAV in flight or in the standby state. In order to measure individual swarm energy consumption, an energy model, suitable for flying robots is used from [38]. In this model, the energy consumption was split into two parts: the electrical power e f required for flight and the electrical power required to be stationary (landed) e s .…”

Section: Swarm Mobility Behaviormentioning

confidence: 99%

Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment

et al. 2018

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Building an ad-hoc network in emergency situations can be crucial as a primary tool or even when used prior to subsequent operations. The use of mini and micro Unmanned Aerial Vehicles (UAVs) is increasing because of the wide range of possibilities they offer. Moreover, they have been proven to bring sustainability to many applications, such as agriculture, deforestation and wildlife conservation, among others. Therefore, creating a UAV network for an unknown environment is an important task and an active research field. In this article, a mobility model for the creation of ad-hoc networks using UAVs will be presented. This model will be based on pheromones for robust navigation. We will focus mainly on developing energy-efficient behavior, which is essential for this type of vehicle. Although there are in the literature several models of mobility for ad-hoc network creation, we find that either they are not adapted to the specific energy requirements of UAVs or the proposed motion models are unrealistic or not sufficiently robust for final implantation. We will present and analyze the operation of a distributed swarm behavior able to create an ad-hoc network. Then, an analytical model of the swarm energy consumption will be proposed. This model will provide a mechanism to effectively predict the energy consumption needed for the deployment of the network prior to its implementation. Determining the use of the mobility behavior is a requirement to establish and maintain a communication channel for the required time. Finally, this analytical model will be experimentally validated and compared to the Random Waypoint (RWP) mobility strategy.

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“…In swarm robotics research field, several works for the covering and track-ing tasks can be found, such as those presented in [6,1,10,21,8,34,35]. However, we have found that many of these works do not take into account the special characteristics of the UAVs and therefore are not energetically applicable [6,10,21,8].…”

Section: Introductionmentioning

confidence: 99%

“…However, we have found that many of these works do not take into account the special characteristics of the UAVs and therefore are not energetically applicable [6,10,21,8]. Others works, although were designed for this type of vehicle, are not applicable to the required localization and tracking tasks and are not adapted to the specific needs of non-structured environments, required in marine exploration and jellyfish blooms detection [34,35]. In this article we will propose a swarm behaviour for UAV, that will be formalized with a microscopic model.…”

Section: Introductionmentioning

confidence: 99%

A swarm behaviour for jellyfish bloom detection

2017

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In this paper we will deal with the issue of swarm behaviour for jellyfish detection using UAVs (Unmanned Aerial Vehicles). Swarm behaviour is inspired by the functioning of biological swarms. They are characterized by being fully distributed, scalable and fault-tolerant. Initially, we will study the behaviour of jellyfish and their impact and interaction with industry. Motivated by the need to improve current detection systems, we will propose a swarm behaviour, that will be formalized with a microscopic model. We will discuss both the convergence and the scalability of the model. Finally, a macroscopic model will be provided to predict the probability that an individual is placed in a position at a given moment.

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Energy-Time Efficiency in Aerial Swarm Deployment

Cited by 15 publications

References 21 publications

Modelling multi-rotor UAVs swarm deployment using virtual pheromones

Modelling multi-rotor UAVs swarm deployment using virtual pheromones

Energy-Efficient Swarm Behavior for Indoor UAV Ad-Hoc Network Deployment

A swarm behaviour for jellyfish bloom detection

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