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Rescooped by Eliseo Ferrante from Papers
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Flocking algorithm for autonomous flying robots

Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. These patterns can be efficiently reconstructed with simple flocking models, based on three simple rules: cohesion of the flock, repulsion of neighbouring individuals and alignment of velocity between neighbours. When designing robot swarms, the controlling dynamics of the robots can be based on these models. In this paper we present such a flocking algorithm endowing flying robots with the capability of self-organized collective manoeuvring. The main new feature of our approach is that we include a term in the velocity alignment part of the equations which is an analogue of the usual frictional force between point-wise bodies. We also introduce a generalized mathematical model of an autonomous flying robot, based on flight field tests. With simulations, we test the flocking algorithm from the aspects of the most general deficiencies of robotic systems, such as time delay, locality of the communication and inaccuracy of the sensors. Some of these deficiencies often cause instabilities and oscillations in the system. We show that the instabilities can be efficiently reduced in all states of the system by the inclusion of the friction-like velocity alignment, resulting in stable flocking flight of the robots.

 

Flocking algorithm for autonomous flying robots
Csaba Virágh, Gábor Vásárhelyi, Norbert Tarcai, Tamás Szörényi, Gergő Somorjai, Tamás Nepusz, Tamás Vicsek

http://arxiv.org/abs/1310.3601


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Evolution of swarming behavior is shaped by how predators attack

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of "domains of danger." Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

 

Evolution of swarming behavior is shaped by how predators attack
Randal S. Olson, David B. Knoester, Christoph Adami

http://arxiv.org/abs/1310.6012


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Information Flow in a Kinetic Ising Model Peaks in the Disordered Phase

There is growing evidence that for a range of dynamical systems featuring complex interactions between large ensembles of interacting elements, mutual information peaks at order-disorder phase transitions. We conjecture that, by contrast, information flow in such systems will generally peak strictly on the disordered side of a phase transition. This conjecture is verified for a ferromagnetic 2D lattice Ising model with Glauber dynamics and a transfer entropy-based measure of systemwide information flow. Implications of the conjecture are considered, in particular, that for a complex dynamical system in the process of transitioning from disordered to ordered dynamics (a mechanism implicated, for example, in financial market crashes and the onset of some types of epileptic seizures); information dynamics may be able to predict an imminent transition.

 

Lionel Barnett, Joseph T. Lizier, Michael Harré, Anil K. Seth, and Terry Bossomaier

"Information Flow in a Kinetic Ising Model Peaks in the Disordered Phase"

Physical Review Letters 111, 177203 (2013)

http://link.aps.org/doi/10.1103/PhysRevLett.111.177203


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Focus on Swarming in Biological and Related Systems

Focus on Swarming in Biological and Related Systems | Collective Intelligence | Scoop.it

In the last 15 years, the collective motion of large numbers of self-propelled objects has become an increasingly active area of research. The examples of such collective motion abound: flocks of birds, schools of fish, swarms of insects, herds of animals etc. Swarming of living creatures is believed to be critical for the population survival under harsh conditions. The ability of motile microorganisms to communicate and coordinate their motion leads to the remarkably complex self-organized structures found in bacterial biofilms. Active intracellular transport of biological molecules within the cytoskeleton has a profound effect on the cell cycle, signaling and motility. In recent years, significant progress has also been achieved in the design of synthetic self-propelled particles. Their collective motion has many advantages for performing specific robotic tasks, such as collective cargo delivery or harvesting the mechanical energy of chaotic motion.

 

http://iopscience.iop.org/1367-2630/focus/Focus%20on%20Swarming%20in%20Biological%20and%20Related%20Systems


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Powerlaws and Self-Organized Criticality in Theory and Nature

Powerlaws and distributions with heavy tails are common features of many experimentally studied complex systems, like the distribution of the sizes of earthquakes and solar flares, or the duration of neuronal avalanches in the brain. It had been tempting to surmise that a single general concept may act as a unifying underlying generative mechanism, with the theory of self organized criticality being a weighty contender.
On the theory side there has been, lively activity in developing new and extended models. Three classes of models have emerged. The first line of models is based on a separation between the time scales of drive and dissipation, and includes the original sandpile model and its extensions, like the dissipative earthquake model. Within this approach the steady state is close to criticality in terms of an absorbing phase transition. The second line of approach is based on external drives and internal dynamics competing on similar time scales and includes the coherent noise model, which has a non-critical steady state characterized by heavy-tailed distributions. The third line of modeling proposes a non-critical state which is self-organizing, being guided by an optimization principle, such as the concept of highly optimized tolerance.
We present a comparative overview regarding distinct modeling approaches together with a discussion of their potential relevance as underlying generative models for real-world phenomena. The complexity of physical and biological scaling phenomena has been found to transcend the explanatory power of individual paradigmal concepts, like the theory of self-organized criticality, the interaction between theoretical development and experimental observations has been very fruitful, leading to a series of novel concepts and insights.

 

Powerlaws and Self-Organized Criticality in Theory and Nature
Dimitrije Markovic, Claudius Gros

http://arxiv.org/abs/1310.5527


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A unifying framework reveals key properties of multilevel selection

A unifying framework reveals key properties of multilevel selection | Collective Intelligence | Scoop.it

A ball-and-urn system is presented as a conceptual tool for understanding multilevel selection.


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Collective motion dynamics of active solids and active crystals

We introduce a simple model of self-propelled particles connected by linear springs that describes a semi-rigid formation of active agents without explicit alignment rules. The model displays a discontinuous transition at a critical noise level, below which the group self-organizes into a collectively translating or rotating state. We identify a novel elasticity-based mechanism that cascades self-propulsion energy towards lower-energy modes as responsible for such collective motion and illustrate it by computing the spectral decomposition of the elastic energy. We study the model's convergence dynamics as a function of system size and derive analytical stability conditions for the translating state in a continuous elastic sheet approximation. We explore the dynamics of a ring-shaped configuration and of local angular perturbations of an aligned state. We show that the elasticity-based mechanism achieves collective motion even in cases with heterogeneous self-propulsion speeds. Given its robustness, simplicity and ubiquity, this mechanism could play a relevant role in various biological and artificial swarms.

 

Collective motion dynamics of active solids and active crystals

Eliseo Ferrante et al 2013 New J. Phys. 15 095011 http://dx.doi.org/10.1088/1367-2630/15/9/095011

Video Abstract: http://iopscience.iop.org/1367-2630/15/9/095011/video/abstract


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