Evidence map›Paper›PMID 42663769›Full record

ArticleThe European physical journal. E, Soft matter2026

Emergent collective dynamics of microrobotic swarms in viscoelastic media: a computational perspective.

Ratnadeep Pramanik

Abstract read
In one paragraph

Article in The European physical journal. E, Soft matter, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

1 author.

Ratnadeep PramanikInstitut für Mathematik und Computergestützte Simulation, Universität der Bundeswehr München, Werner-Heisenberg-Weg 39, 85577, Neubiberg, Germany. ratnadeep.pramanik@unibw.de.ORCID http://orcid.org/0000-0002-3310-2172

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microrobotic swarms are promising candidates for targeted drug delivery in complex physiological environments, including blood, mucus, and extracellular matrices. In such biomedical scenarios, collective motion is strongly influenced by low-Reynolds-number drag, crowding, confinement, viscoelasticity, and non-Newtonian rheology. This article envisages a mechanics-based computational framework for microrobotic swarms in viscoelastic (gel-like) media. Starting from Langevin dynamics, we derive the overdamped description appropriate for microscale agents and extend it to active Brownian particles with self-propulsion, local alignment, and short-range interparticle interactions. Cohesive and purely repulsive collective regimes are represented through Lennard-Jones and Weeks-Chandler-Andersen potentials, respectively. To model gel-like rheology, we introduce a fractional Kelvin-Voigt description of drag that incorporates power-law memory effects. The resulting stochastic dynamics are mapped to an explicit Euler-Maruyama algorithm with truncated-history fractional convolution, documented stabilizers, and diagnostic observables for polarization, clustering, trajectories, and transport. Beyond the model itself, the present study synthesizes recent work on bacterial living fluids, externally driven colloidal and microrobotic swarms, fish-school hydrodynamics, neural-network control of collective patterns, and cross-scale magnetic catheter-swarm thrombus removal. That microrobotic-swarm transport in biomedical media should not be treated only as a soft-matter problem is the primary takeaway. We believe that it is rather a coupled mechanics problem in which propulsion, interaction, memory, disorder, hydrodynamic communication, and clinical deliverability must be taken into consideration. At the same time, the preliminary simulations suggest a practically important biomedical trend: whereas Newtonian transport is more prone to collective aggregation, viscoelastic transport can preserve a more distributed swarm morphology, which is encouraging for controllable delivery, broader spatial coverage, and aggregation-resistant payload transport in complex bodily fluids.

Indexed as

ElasticityRoboticsComputer SimulationRheologyViscosity

Identifiers

PMID42663769
PMCPMC13525069

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.