Abstract
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.
Highlights
Related to Unmanned Aerial Vehicles (UAVs) deployment, we found one of the first applications presented in [27]
The macroscopic model correctly predicts these environments without much variation with respect to structured maps. This result is expected, since no information regarding the structure of the environment is included in the microscopic model
A microscopic model for a swarm behavior has been presented in this paper
Summary
Multi-rotor UAVs swarm deployment limits of single robots, perform a complex task with multiple specialized simple robots rather than a super robot, and provide distributed, parallel mobile sensing and processing. The ants have simple sensor ability, limited computational power and a decentralized control system These features make it practical to build a large number of ant-like robots to explore an unknown terrain. Once we discuss the microscopic behaviour of the swarm we will introduce a macroscopic model. This mathematical model predicts the number of individuals that execute each of the behaviour states, which allows us to forecast the system performance without simulating or testing the behaviour. We will finish by providing the conclusions of this work and some future lines
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