Self-swarming robots that exploit hydrodynamical interaction

Fujiwara, Ryo; Kano, Takeshi; Ishiguro, Akio

doi:10.1080/01691864.2013.879365

Cited by 8 publications

(5 citation statements)

References 23 publications

(32 reference statements)

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“…In principle, our predictions can be tested in experiments on macroscopic self-propelled particles or mesoscopic particles in a gaseous background. Examples from the inanimate macroscopic world include autorotating seeds and fruits [50,75], camphor surfers [60], hexbug crawlers [60], trapped aerosols [76], mini-robots [77][78][79][80][81] and vibration-driven granular particles [57][58][59][82][83][84][85][86][87][88][89][90]. For the latter system dumbbell-like inertial active particles have been recently realized [91].…”

Section: Discussionmentioning

confidence: 99%

Active Ornstein–Uhlenbeck model for self-propelled particles with inertia

Nguyen

Wittmann

Löwen

2021

J. Phys.: Condens. Matter

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Self-propelled particles, which convert energy into mechanical motion, exhibit inertia if they have a macroscopic size or move inside a gaseous medium, in contrast to micron-sized overdamped particles immersed in a viscous fluid. Here we study an extension of the active Ornstein–Uhlenbeck model, in which self-propulsion is described by colored noise, to access these inertial effects. We summarize and discuss analytical solutions of the particle’s mean-squared displacement and velocity autocorrelation function for several settings ranging from a free particle to various external influences, like a linear or harmonic potential and coupling to another particle via a harmonic spring. Taking into account the particular role of the initial particle velocity in a nonstationary setup, we observe all dynamical exponents between zero and four. After the typical inertial time, determined by the particle’s mass, the results inherently revert to the behavior of an overdamped particle with the exception of the harmonically confined systems, in which the overall displacement is enhanced by inertia. We further consider an underdamped model for an active particle with a time-dependent mass, which critically affects the displacement in the intermediate time-regime. Most strikingly, for a sufficiently large rate of mass accumulation, the particle’s motion is completely governed by inertial effects as it remains superdiffusive for all times.

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Section: Discussionmentioning

confidence: 99%

Active Ornstein–Uhlenbeck model for self-propelled particles with inertia

Nguyen

Wittmann

Löwen

2021

J. Phys.: Condens. Matter

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“…Understanding motility requires connections between fundamental physics and biology [1][2][3] and has many applications, including drug-delivering nanomachines [4,5] and autonomous underwater vehicles [6][7][8]. Swimming regimes can be classified by the Reynolds number (Re), which characterizes the relative importance of inertial over viscous forces.…”

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confidence: 99%

Transition in swimming direction in a model self-propelled inertial swimmer

et al. 2019

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We propose a reciprocal, self-propelled model swimmer at intermediate Reynolds numbers (Re). Our swimmer consists of two unequal spheres that oscillate in antiphase generating nonlinear steady streaming (SS) flows. We show computationally that the SS flows enable the swimmer to propel itself, and also switch direction as Re increases. We quantify the transition in the swimming direction by collapsing our data on a critical Re and show that the transition in swimming directions corresponds to the reversal of the SS flows. Based on our findings, we propose that SS can be an important physical mechanism for motility at intermediate Re.PACS numbers: May be entered using the \pacs{#1} command. arXiv:1801.03974v2 [physics.flu-dyn]

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“…Thirdly, the findings suggest that a biomimicry approach to designing mass collaborative systems may be fruitful. While swarm intelligence is currently used in areas like social learning [43] and navigation of robots [44], relatively little research has been done to extend this concept further into human decision making. For example, future research may investigate how swarming systems might enable human-AI collaboration [45], whereby human and non-human agents collaborate together to converge upon optional decisions.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Askay

Metcalf

Rosenberg

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms, where collective intelligence has been observed to emerge. The present paper describes swarm.ai-a collaborative technology that enables swarms of humans to collectively converge upon a decision as a real-time system. Then we present the results of a study investigating if groups working as "human swarms" can amplify their social perceptiveness, a key predictor of collective intelligence. Results showed that groups reduced their social perceptiveness errors by more than half when operating as a swarm. A statistical analysis revealed with 99.9% confidence that groups working as swarms had significantly higher social perceptiveness than either individuals working alone or through plurality vote.

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Self-swarming robots that exploit hydrodynamical interaction

Cited by 8 publications

References 23 publications

Active Ornstein–Uhlenbeck model for self-propelled particles with inertia

Active Ornstein–Uhlenbeck model for self-propelled particles with inertia

Transition in swimming direction in a model self-propelled inertial swimmer

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

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