Collective motion from local attraction

Strömbom, Daniel

doi:10.1016/j.jtbi.2011.05.019

Cited by 130 publications

(156 citation statements)

References 23 publications

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“…One explanation is that fish are attracted more strongly to neighbors in front of them than those behind, producing an asymmetry of leading and following that promotes group alignment (23). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Instead, it appears that group alignment is achieved in some other way, possibly through attraction and repulsion rules and by fish following individuals in front of themselves. Models have already shown that such interactions can produce highly polarized groups (23,27). However, this is not to say that alignment rules are never adopted by this or other species.…”

Section: Discussionmentioning

confidence: 99%

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Inferring the rules of interaction of shoaling fish

Herbert‐Read

Perna

Mann

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

514

550

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Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors' movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor's orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move.collective animal behavior | fish shoals | group motion | self-propelled particles | self-organization C ollective motion of animal groups occurs when multiple individuals move synchronously, producing large scale "flocking" patterns (1-5). Numerous models have been developed to describe patterns of collective motion in terms of interactions between individuals (6-9). These simulation models usually assume that individuals move at a constant speed and their interactions are mediated through direction changes (1). Often these models use zonal rules, where individuals move away from neighbors at close distances and align and/or move toward neighbors at greater distances. Interactions can be with either all neighbors within some zone (7) or with a set of n nearest neighbors (10). These and other models have succeeded in reproducing qualitatively similar patterns to those seen in the collective motion of animal groups in nature.It remains unclear, however, whether the interaction rules implemented in models are the ones used by animals. Indeed, many collective motion patterns observed in nature can be simulated by models using very different interaction rules (1). We are only now beginning to accumulate evidence about which interaction rules are used. There has been recent identification of zones of repulsion and alignment in surf scoters (11). The structure of starling flocks is consistent with topological interactions between the birds (10). Homing pigeons appear to have hierarchical interactions such that birds with higher route-following fidelity act as leaders (12)(13)(14)(15). Partridge showed that lateral line and vision are both important in producing directional alignment in Gadid fish (16). Nonetheless, there remain a large number of open questions about the interactions of fish. For example, do fish adopt attraction and alignment within distinct zones as purported in most models? How many neighbors do fish ...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Inferring the rules of interaction of shoaling fish

Herbert‐Read

Perna

Mann

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

514

550

View full text Add to dashboard Cite

show abstract

“…Models with only attractive and repulsive interactions have long been studied in biology in the context of group formation and swarming [26 -28]. Still, only relatively few models have focused on the onset of collective motion without alignment based on purely repulsive and attractive interactions [29][30][31][32][33][34]. This is most probably because velocity alignment provides a simple and robust mechanism for the onset of polarized swarms, where individuals are able to agree on a common direction of motion.…”

Section: Introductionmentioning

confidence: 99%

Swarming and pattern formation due to selective attraction and repulsion

Romanczuk

Schimansky‐Geier

2012

Interface Focus.

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We discuss the collective dynamics of self-propelled particles with selective attraction and repulsion interactions. Each particle, or individual, may respond differently to its neighbours depending on the sign of their relative velocity. Thus, it is able to distinguish approaching (coming closer) and retreating (moving away) individuals. This differentiation of the social response is motivated by the response to looming visual stimuli and may be seen as a generalization of the previously proposed escape and pursuit interactions motivated by empirical evidence for cannibalism as a driving force of collective migration in locusts and Mormon crickets. The model can account for different types of behaviour such as pure attraction, pure repulsion or escape and pursuit, depending on the values (signs) of the different response strengths. It provides, in the light of recent experimental results, an interesting alternative to previously proposed models of collective motion with an explicit velocity -alignment interaction. We discuss the derivation of a coarse-grained description of the system dynamics, which allows us to derive analytically the necessary condition for emergence of collective motion. Furthermore, we analyse systematically the onset of collective motion and clustering in numerical simulations of the model for varying interaction strengths. We show that collective motion arises only in a subregion of the parameter space, which is consistent with the analytical prediction and corresponds to an effective escape and/or pursuit response.

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“…The model described in this paper is one of a small set of swarm models that exhibit multiple group behaviors [9,29,27]. This research concentrates on the flock and torus behaviors; however, there are other types of swarm behavior seen in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…Couzin's swarm behavior is analogous to a torus without orientation in our model, and the dynamic and highly parallel groups are simply two flavors of flocking. Strömbom [29] demonstrates that attractive forces between agents are sufficient to form swarms, flocks, and mills (torus-like formations where agents do not all rotate in the same direction). Strömbom also shows that adding a blind spot creates two additional group types: a torus and an interweaving chain-like structure.…”

Section: Related Workmentioning

confidence: 99%

Human-swarm interactions based on managing attractors

Brown

Kerman

Goodrich

2014

Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction

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Leveraging the abilities of multiple affordable robots as a swarm is enticing because of the resulting robustness and emergent behaviors of a swarm. However, because swarms are composed of many different agents, it is difficult for a human to influence the swarm by managing individual agents. Instead, we propose that human influence should focus on (a) managing the higher level attractors of the swarm system and (b) managing trade-offs that appear in mission-relevant performance. We claim that managing attractors theoretically allows a human to abstract the details of individual agents and focus on managing the collective as a whole. Using a swarm model with two attractors, we demonstrate this concept by showing how limited human influence can cause the swarm to switch between attractors. We further claim that using quorum sensing allows a human to manage tradeoffs between the scalability of interactions and mitigating the vulnerability of the swarm to agent failures.

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Collective motion from local attraction

Cited by 130 publications

References 23 publications

Inferring the rules of interaction of shoaling fish

Inferring the rules of interaction of shoaling fish

Swarming and pattern formation due to selective attraction and repulsion

Human-swarm interactions based on managing attractors

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