Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

Vigelius, M.; Meyer, Bernd; Pascoe, Geoffrey

doi:10.1371/journal.pone.0111542

Cited by 23 publications

(17 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To enable swarming, it is essential to achieve distributed communication and decentralized decision-making Valentini et al, 2014Valentini et al, , 2015Valentini et al, , 2017Vigelius et al, 2014). For instance, natural swarms achieve self-organizing behaviors and decentralized decision-making by means of distributed information exchanges through local signaling (Camazine et al, 2001) associated with sometimes sophisticated signaling mechanisms and trophic interactions (Dusenbery, 1992).…”

Section: Methodsmentioning

confidence: 99%

Swarm-Enabling Technology for Multi-Robot Systems

et al. 2017

View full text Add to dashboard Cite

Swarm robotics has experienced a rapid expansion in recent years, primarily fueled by specialized multi-robot systems developed to achieve dedicated collective actions. These specialized platforms are, in general, designed with swarming considerations at the front and center. Key hardware and software elements required for swarming are often deeply embedded and integrated with the particular system. However, given the noticeable increase in the number of low-cost mobile robots readily available, practitioners and hobbyists may start considering to assemble full-fledged swarms by minimally retrofitting such mobile platforms with a swarm-enabling technology. Here, we report one possible embodiment of such a technology-an integrated combination of hardware and software-designed to enable the assembly and the study of swarming in a range of general-purpose robotic systems. This is achieved by combining a modular and transferable software toolbox with a hardware suite composed of a collection of low-cost and off-theshelf components. The developed technology can be ported to a relatively vast range of robotic platforms-such as land and surface vehicles-with minimal changes and high levels of scalability. This swarm-enabling technology has successfully been implemented on two distinct distributed multi-robot systems, a swarm of mobile marine buoys and a team of commercial terrestrial robots. We have tested the effectiveness of both of these distributed robotic systems in performing collective exploration and search scenarios, as well as other classical cooperative behaviors. Experimental results on different swarm behaviors are reported for the two platforms in uncontrolled environments and without any supporting infrastructure. The design of the associated software library allows for a seamless switch to other cooperative behaviors-e.g., leader-follower heading consensus and collision avoidance, and also offers the possibility to simulate newly designed collective behaviors prior to their implementation onto the platforms. This feature greatly facilitates behavior-based design, i.e., the design of new swarming behaviors, with the possibility to simulate them prior to physically test them.

show abstract

Section: Methodsmentioning

confidence: 99%

Swarm-Enabling Technology for Multi-Robot Systems

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This is a very difficult task since there is no general systematic way to devise individual behaviours that reliably achieve a desired group behaviour. Thus design choices can usually only be tested in experiments or simulations [37]. …”

Section: Platformmentioning

confidence: 99%

Modelling multi-rotor UAVs swarm deployment using virtual pheromones

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

“…This is a very di cult task since there is no general systematic way to devise individual behaviours that reliably achieve a desired group behaviour. Thus design choices can usually only be tested in experiments or simulations [38].…”

Section: Microscopic Modelmentioning

confidence: 99%

“…Second, physical experiments are expensive, time-consuming and can usually only be conducted under sanitized laboratory conditions. Third, deriving macroscopic descriptions from probabilistic microscopic ones is usually hard, in particular if spatial aspects need to be taken into account [38].…”

Section: Macroscopic Modelmentioning

confidence: 99%

A swarm behaviour for jellyfish bloom detection

2017

View full text Add to dashboard Cite

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.

show abstract

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

Cited by 23 publications

References 28 publications

Swarm-Enabling Technology for Multi-Robot Systems

Swarm-Enabling Technology for Multi-Robot Systems

Modelling multi-rotor UAVs swarm deployment using virtual pheromones

A swarm behaviour for jellyfish bloom detection

Contact Info

Product

Resources

About