Dynamic coordinated control laws in multiple agent models

We present an active control scheme of a kinetic model of swarming. It has been shown previously that the global control scheme for the model, presented in \cite{JK04}, gives rise to spontaneous collective organization of agents into a unified coherent swarm, via a long-range attractive and short-range repulsive potential. We extend these results by presenting control laws whereby a single swarm is broken into independently functioning subswarm clusters. The transition between one coordinated swarm and multiple clustered subswarms is managed simply with a homotopy parameter. Additionally, we present as an alternate formulation, a local control law for the same model, which implements dynamic barrier avoidance behavior, and in which swarm coherence emerges spontaneously.Comment: 20 pages, 6 figure

A Neural-Endocrine Architecture for Foraging in Swarm Robotic Systems

Abstract This paper presents the novel use of the Neural-endocrine architecture for swarm robotic systems. We make use of a number of behaviours to give rise to emergent swarm behaviour to allow a swarm of robots to collaborate in the task of foraging. Results show that the architecture is amenable to such a task, with the swarm being able to successfully complete the required task.

Onboard Evolution of Understandable Swarm Behaviors

Designing the individual robot rules that give rise to desired emergent swarm behaviors is difficult. The common method of running evolutionary algorithms off‐line to automatically discover controllers in simulation suffers from two disadvantages: the generation of controllers is not situated in the swarm and so cannot be performed in the wild, and the evolved controllers are often opaque and hard to understand. A swarm of robots with considerable on‐board processing power is used to move the evolutionary process into the swarm, providing a potential route to continuously generating swarm behaviors adapted to the environments and tasks at hand. By making the evolved controllers human‐understandable using behavior trees, the controllers can be queried, explained, and even improved by a human user. A swarm system capable of evolving and executing fit controllers entirely onboard physical robots in less than 15 min is demonstrated. One of the evolved controllers is then analyzed to explain its functionality. With the insights gained, a significant performance improvement in the evolved controller is engineered

Efficient 3D Placement of a UAV Using Particle Swarm Optimization

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.Comment: 6 pages, 7 figure

Density regulation in strictly metric-free swarms

There is now experimental evidence that nearest-neighbour interactions in flocks of birds are metric free, i.e. they have no characteristic interaction length scale. However, models that involve interactions between neighbours that are assigned topologically are naturally invariant under spatial expansion, supporting a continuous reduction in density towards zero, unless additional cohesive interactions are introduced or the density is artificially controlled, e.g. via a finite system size. We propose a solution that involves a metric-free motional bias on those individuals that are topologically identified to be on an edge of the swarm. This model has only two primary control parameters, one controlling the relative strength of stochastic noise to the degree of co-alignment and another controlling the degree of the motional bias for those on the edge, relative to the tendency to co-align. We find a novel power-law scaling of the real-space density with the number of individuals N as well as a familiar order-to-disorder transition

Direct numerical simulation of the drag force in bubble swarms

This paper studies the swarm effect on the drag force in bubbly flows. From literature it is well-known that for relatively small bubbles, the drag force increases with the bubble hold-up due to additional hindrance experienced by the bubbles caused by the modified flow field. Very large (spherical cap) bubbles on the other hand may rise cooperatively. The unique capabilities of a 3D Front Tracking model were used to investigate the influence of important parameters like the gas fraction, Reynolds number and the bubble size independently. It was found that the relative drag force increases for bubbles in the range of 2 to 5 mm when the gas fraction is increased up to 13%, while the bubbles become more spherical. Also the influence of the Reynolds number and the bubble aspect ratio on the increased drag force has been determined. It can be concluded that there is only a very weak effect over several decades of the Reynolds number, while there is a strong effect of the bubble aspect ratio.\ud This also helps explaining why the increase in drag is smaller for larger bubbles: when the gas fraction is increased deformable bubbles become more spherical, thus reducing the drag force

The Role of Projection in the Control of Bird Flocks

Swarming is a conspicuous behavioural trait observed in bird flocks, fish shoals, insect swarms and mammal herds. It is thought to improve collective awareness and offer protection from predators. Many current models involve the hypothesis that information coordinating motion is exchanged between neighbors. We argue that such local interactions alone are insufficient to explain the organization of large flocks of birds and that the mechanism for the exchange of long-ranged information necessary to control their density remains unknown. We show that large flocks self-organize to the maximum density at which a typical individual is still just able to see out of the flock in many directions. Such flocks are marginally opaque - an external observer can also just still see a substantial fraction of sky through the flock. Although seemingly intuitive we show that this need not be the case; flocks could easily be highly diffuse or entirely opaque. The emergence of marginal opacity strongly constrains how individuals interact with each other within large swarms. It also provides a mechanism for global interactions: An individual can respond to the projection of the flock that it sees. This provides for faster information transfer and hence rapid flock dynamics, another advantage over local models. From a behavioural perspective it optimizes the information available to each bird while maintaining the protection of a dense, coherent flock.Comment: PNAS early edition published online at http://www.pnas.org/cgi/doi/10.1073/pnas.140220211