Dynamics of prey-flock escaping behavior in response to predator's attack

Lee, S.-H.; Pak, Hyuk Kyu; Chon, Tae‐Soo

doi:10.1016/j.jtbi.2005.09.009

Cited by 61 publications

(39 citation statements)

References 34 publications

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“…If the model is known, together with the response of the swarm to an adversary, it would be possible to not just design optimal strategies to divert or control the swarm, but also derive guarantees on the performance of such strategies. In [153], experimental data was used to identify a model, based on [154], [155], for the response of a flock of birds to a UAV located within a certain range of the flock. The approach adopted in [153] works for flocks whose response to perturbations is based on a static, deterministic law.…”

Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: E External Control Of Aerial Swarmsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Evasive strategies used by flocks against one or more predators are presented in [28], [29]. Escape waves, as a means for formulating the evasive response, have been examined for starling flocks [30] and fish schools [31].…”

Section: A Overview Of the Literaturementioning

confidence: 99%

“…We use a standard flocking model, similar to Reynolds [9], [17], augmented with evasion laws similar to [28], [29]. It is assumed that the pursuer has access to real-time data about the flock, such as that provided by avian radars [41].…”

Section: B Objectives and Contributionsmentioning

confidence: 99%

“…It is well known that a flock tends to deform into a concave shape locally under persistent pressure from a pursuer, which is known to dent the effectiveness of the pursuit [29]. Furthermore, over stressing one or more birds continuously over an extended period of time carries the risk of the distressed birds attempting aggressive evasive maneuvers and fragmenting the flock.…”

Section: B Determination Of M Waypointsmentioning

confidence: 99%

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Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

et al. 2018

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Abstract-In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the mwaypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds' rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.

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“…Next, new images created using generative models are classified using the manifold-to-feature mapping. We then analyze and classify images of self-propelled particles interacting via a modified Vicsek model [33], where the particles randomly change their speed to simulate speed variations observed in animal groups [34,35]. Finally, the approach is validated on experimental videos of zebrafish schooling in laboratory controlled environments obtained from the literature [36].…”

Section: Introductionmentioning

confidence: 99%

Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning

Butail

Bollt

Porfiri

2013

Journal of Theoretical Biology

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In this paper, we build a framework for the analysis and classification of collective behavior using methods from generative modeling and nonlinear manifold learning. We represent an animal group with a set of finite-sized particles and vary known features of the group structure and motion via a class of generative models to position each particle on a two-dimensional plane. Particle positions are then mapped onto training images that are processed to emphasize the features of interest and match attainable far-field videos of real animal groups. The training images serve as templates of recognizable patterns of collective behavior and are compactly represented in a low-dimensional space called embedding manifold. Two mappings from the manifold are derived: the manifold-to-image mapping serves to reconstruct new and unseen images of the group and the manifold-to-feature mapping allows frame-by-frame classification of raw video. We validate the combined framework on datasets of growing level of complexity. Specifically, we classify artificial images from the generative model, interacting self-propelled particle model, and raw overhead videos of schooling fish obtained from the literature.

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Dynamics of prey-flock escaping behavior in response to predator's attack

Cited by 61 publications

References 34 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle

Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning

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