Obstacle and predator avoidance in a model for flocking

Mecholsky, Nicholas A.; Ott, Edward; Antonsen, Thomas M.

doi:10.1016/j.physd.2010.02.007

Cited by 25 publications

(25 citation statements)

References 27 publications

(35 reference statements)

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“…One study allowed pursuer and evader visual systems to evolve computationally in response to the outcomes of simulated pursuits and captures; this work found that the angle for optimal vision for pursuers agreed with the observed orientation of the shallow or the deep fovea (Cliff and Miller, 1996). In the absence of empirical data, computational models of predation on flocks (Ballerini et al, 2008a;Ballerini et al, 2008b;Lee, 2006;Lee et al, 2006;Mecholsky et al, 2010) have assumed classical pursuit. These results should be revisited to model how likely pursuit strategies used by falcons on single birds influence their attacks on flocks.…”

Section: Research Articlementioning

confidence: 99%

Falcons pursue prey using visual motion cues: new perspectives from animal-borne cameras

Kane

Zamani

2014

Journal of Experimental Biology

116

107

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This study reports on experiments on falcons wearing miniature videocameras mounted on their backs or heads while pursuing flying prey. Videos of hunts by a gyrfalcon (Falco rusticolus), gyrfalcon (F. rusticolus)/Saker falcon (F. cherrug) hybrids and peregrine falcons (F. peregrinus) were analyzed to determine apparent prey positions on their visual fields during pursuits. These video data were then interpreted using computer simulations of pursuit steering laws observed in insects and mammals. A comparison of the empirical and modeling data indicates that falcons use cues due to the apparent motion of prey on the falcon's visual field to track and capture flying prey via a form of motion camouflage. The falcons also were found to maintain their prey's image at visual angles consistent with using their shallow fovea. These results should prove relevant for understanding the co-evolution of pursuit and evasion, as well as the development of computer models of predation and the integration of sensory and locomotion systems in biomimetic robots. KEY WORDS: Predation, Pursuit-evasion, Avian vision, Falcon, Motion camouflage INTRODUCTIONHow predators track and capture their prey poses a fundamental problem in animal behavior that combines sensory perception, neural computation and locomotion. Empirical studies in combination with computational modeling (Pais and Leonard, 2010;Reddy et al., 2006;Srinivasan and Davey, 1995) have identified pursuit strategies used by insects (Olberg, 2012), bats (Ghose et al., 2006), dogs (Shaffer et al., 2004), fish (Lanchester and Mark, 1975) and humans (Fajen and Warren, 2004;McBeath et al., 1995). However, no empirical studies have addressed how falcons and other birds pursue flying prey, mostly due to the difficulty of recording their 3D flight trajectories in the field. The pursuit strategies used by birds have evolved in the context of their unique flight capabilities, as well as their need to pursue rapid, erratically moving prey in complex environments. Understanding their methods for tracking and following rapidly moving objects should provide inspiration for the design of unmanned aerial vehicles (UAVs) and other biomimetic robots.Stereoscopic video methods successfully used to study flocking (Ballerini et al., 2008a;Cavagna et al., 2010;Cavagna et al., 2008) are challenging to apply to this problem because of the wide geographic areas covered and the rapid, unpredictable motion of both predator and prey. However, bird-mounted sensors offer new opportunities for this field. While miniaturized, bird-mounted GPS sensors have been used to study navigation, flocking energetics and decision making in bird flocks (Biro et al., 2006;Nagy et al., 2010;Steiner et al., 2000;Usherwood et al., 2011), the 10 Hz data collection rate and 0.3 m spatial resolution of the GPS are of limited use in the present context. By contrast, miniaturized bird-mounted cameras have proved effective in studying the aerodynamics of avian flight (Carruthers et al., 2007;Gillies et al., 2011) and bird ...

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Section: Research Articlementioning

confidence: 99%

Falcons pursue prey using visual motion cues: new perspectives from animal-borne cameras

Kane

Zamani

2014

Journal of Experimental Biology

116

107

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“…Several other variants and generalization have been proposed including more general potentials, cone-vision constraints, leadership (see e.g. [15,17,27,37,40,46,50,52]), stochastic terms ( [18,23,25]), pedestrian crowds (see [16,31]), infinite-dimensional kinetic models (see [1,4,9,12,22,28,49]), topological models ( [5,29]), control models (see [6,10,11,43,51]).…”

Section: Introductionmentioning

confidence: 99%

Flocking estimates for the Cucker–Smale model with time lag and hierarchical leadership

Pignotti

Vallejo

2018

Journal of Mathematical Analysis and Applications

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We analyze the Cucker-Smale model under hierarchical leadership in presence of a time delay. By using a Lyapunov functional approach and some induction arguments we will prove convergence to consensus for every positive delay τ. We also prove a flocking estimate in the case of a free-will leader. These results seem to point out the advantage of a hierarchical structure in order to contrast time delay effects that frequently appear in real situations.

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“…Models with simple rules have been shown to reproduce, at least qualitatively, patterns and behaviours observed in the wild, including bulk alignment or polarisation10, milling11, swarming12, aggregation13, and predator avoidance14. Both continuum15 and discrete16 models can produce results that resemble observational data.…”

mentioning

confidence: 98%

Emergent dynamics of laboratory insect swarms

Kelley

Ouellette

2013

Sci Rep

138

263

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Collective animal behaviour occurs at nearly every biological size scale, from single-celled organisms to the largest animals on earth. It has long been known that models with simple interaction rules can reproduce qualitative features of this complex behaviour. But determining whether these models accurately capture the biology requires data from real animals, which has historically been difficult to obtain. Here, we report three-dimensional, time-resolved measurements of the positions, velocities, and accelerations of individual insects in laboratory swarms of the midge Chironomus riparius. Even though the swarms do not show an overall polarisation, we find statistical evidence for local clusters of correlated motion. We also show that the swarms display an effective large-scale potential that keeps individuals bound together, and we characterize the shape of this potential. Our results provide quantitative data against which the emergent characteristics of animal aggregation models can be benchmarked.

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Obstacle and predator avoidance in a model for flocking

Cited by 25 publications

References 27 publications

Falcons pursue prey using visual motion cues: new perspectives from animal-borne cameras

Falcons pursue prey using visual motion cues: new perspectives from animal-borne cameras

Flocking estimates for the Cucker–Smale model with time lag and hierarchical leadership

Emergent dynamics of laboratory insect swarms

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