Collective clog control: Optimizing traffic flow in confined biological and robophysical excavation
When fewer workers are more efficient
A narrow passageway can easily become clogged or jammed if too much traffic tries to enter at once or there is competition between the flow of traffic in each direction. Aguilar et al. studied the collective excavation observed when ants build their nests. Because of the unequal workload distribution, the optimal excavation rate is achieved when a part of the ant collective is inactive. Numerical simulations and the behavior of robotic ants mimic the behavior of the colony.
Science, this issue p. 672
Abstract
Groups of interacting active particles, insects, or humans can form clusters that hinder the goals of the collective; therefore, development of robust strategies for control of such clogs is essential, particularly in confined environments. Our biological and robophysical excavation experiments, supported by computational and theoretical models, reveal that digging performance can be robustly optimized within the constraints of narrow tunnels by individual idleness and retreating. Tools from the study of dense particulate ensembles elucidate how idleness reduces the frequency of flow-stopping clogs and how selective retreating reduces cluster dissolution time for the rare clusters that still occur. Our results point to strategies by which dense active matter and swarms can become task capable without sophisticated sensing, planning, and global control of the collective.
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Supplementary Material
Summary
Materials and Methods
Figs. S1 to S26
Tables S1 to S4
Movies S1 to S5
Resources
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Published In
Science
Volume 361 | Issue 6403
17 August 2018
17 August 2018
Copyright
Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
This is an article distributed under the terms of the Science Journals Default License.
Submission history
Received: 6 April 2017
Accepted: 14 June 2018
Published in print: 17 August 2018
Acknowledgments
The authors thank W. Gardner, Griffin Botanical Garden, and Chattahoochee-Oconee National Forest for giving us permission for ant collection. We would also like to acknowledge N. Gravish for insight and fruitful discussions; M. Kingsbury and L. Chen for assistance in magnetic particle construction; R. Kutner, R. Srivastava, and J. Logan for their help with video analysis; and N. Conn for help with ant collection. H.-S.K. thanks the Max Planck Institute for the Physics of Complex Systems for providing computing resources. Funding: The authors acknowledge the support of National Science Foundation grants NSF PoLS-0957659, PHY-1205878, and DMR-1551095, as well as ARO grant W911NF-13-1-0347, the National Academies Keck Futures Initiative, and the Dunn Family Professorship (to D.I.G.). Author contributions: B.D. and D.M. collected the raw data for the ant experiments. W.S. and D.M. developed and performed the CA simulations. H.-S.K. and M.D.B. developed and analyzed the OAT model. V.L. constructed and performed the ant robot experiments, and J.A. tracked and analyzed the robot experiment data. All authors contributed to the preparation of the manuscript and were involved in the interpretation of results. Competing interests: The authors declare that they have no competing interests. Data and materials availability: Data are available on SMARTech at https://smartech.gatech.edu/.
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Funding Information
National Science Foundation: PoLS-0957659
National Science Foundation: PHY-1205878
National Science Foundation: DMR-1551095
National Science Foundation: DMR-1551095
Army Research Office: W911NF-13-1-0347
National Academies of Sciences, Engineering, and Medicine: National Academies Keck Futures Initiative
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