Fly Swarms and Complexity

A system is considered complex if it is composed of individual parts that abide by their own set of rules while the system, as a whole, exhibits unexpected properties. The motivation for studying complexity spurs from the fact that it is a fundamental aspect of many systems, including forest fires, earthquakes, stock markets, fish schools, plant root growth, and fly swarms. We are particularly interested in fly swarms and the possible complex properties that the swarm exhibits, arising from the individual fly interactions. Fly swarms are a relatively simple complex system, but such systems are still not fully understood. In this research, various computational models were developed to assist with the understanding of fly swarms. These models were primarily described by analyzing the average distance from the center of mass, average distance between flies, and the inertia ratios. The inertia ratios indicated asymmetric fly systems, suggesting some accuracy in such models as physical fly swarms exhibit asymmetry. A major goal of this research was to provide a mathematical definition for swarming. While an arbitrary definition was developed, future research is required to pinpoint a definite definition

Noise-induced breakdown of coherent collective motion in swarms

We consider swarms formed by populations of self-propelled particles with attractive long-range interactions. These swarms represent multistable dynamical systems and can be found either in coherent traveling states or in an incoherent oscillatory state where translational motion of the entire swarm is absent. Under increasing the noise intensity, the coherent traveling state of the swarms is destroyed and an abrupt transition to the oscillatory state takes place.Comment: 6 pages, 5 figures; to appear in Phys. Rev.

Towards human control of robot swarms

In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms

Nonequilibrium statistical mechanics of swarms of driven particles

As a rough model for the collective motions of cells and organisms we develop here the statistical mechanics of swarms of self-propelled particles. Our approach is closely related to the recently developed theory of active Brownian motion and the theory of canonical-dissipative systems. Free motion and motion of a swarms confined in an external field is studied. Briefly the case of particles confined on a ring and interacting by repulsive forces is studied. In more detail we investigate self-confinement by Morse-type attracting forces. We begin with pairs N = 2; the attractors and distribution functions are discussed, then the case N > 2 is discussed. Simulations for several dynamical modes of swarms of active Brownian particles interacting by Morse forces are presented. In particular we study rotations, drift, fluctuations of shape and cluster formation.Comment: 11 pages, 2 figure

A scanning drift tube apparatus for spatio-temporal mapping of electron swarms

A "scanning" drift tube apparatus, capable of mapping of the spatio-temporal evolution of electron swarms, developing between two plane electrodes under the effect of a homogeneous electric field, is presented. The electron swarms are initiated by photoelectron pulses and the temporal distributions of the electron flux are recorded while the electrode gap length (at a fixed electric field strength) is varied. Operation of the system is tested and verified with argon gas, the measured data are used for the evaluation of the electron bulk drift velocity. The experimental results for the space-time maps of the electron swarms - presented here for the first time - also allow clear observation of deviations from hydrodynamic transport. The swarm maps are also reproduced by particle simulations

On Steering Swarms

The main contribution of this paper is a novel method allowing an external observer/controller to steer and guide swarms of identical and indistinguishable agents, in spite of the agents' lack of information on absolute location and orientation. Importantly, this is done via simple global broadcast signals, based on the observed average swarm location, with no need to send control signals to any specific agent in the swarm