Emergent Swarm States and Evolutionary Dynamics in Bacteria-Inspired Robot Swarms

ORAL  · Invited

Abstract

Real-world phenomena are inherently complex and often irreducible to just a few basic principles. Nevertheless, intelligent behavior can still emerge in biological systems, shaped by billions of years of evolution, to solve natural challenges. Drawing inspiration from biological capabilities, in this talk I explore the potential of swarm robotics -- a form of intelligent active matter where each individual unit is capable of sensing, computing, making decisions, and can be fully tailored for enhanced control and adaptability. I will show a robotic community where autonomous robots, equipped with bio-inspired functions, move across a programmable adaptive landscape. These robots can self-organized to optimize resource consumption and survive stressful conditions by emulating organic biology, exhibiting what we term "robobiology". There, novel swarm states of active matters [1] and new insights into the evolutionary dynamics of highly mutable populations [2] were found.

[1] Schirber, M., Robot Forager, Physics magazine (2021)

https://physics.aps.org/articles/v14/38

[2] Day, C., Evolving robots could optimize chemotherapy, Physics Today (2022)

https://doi.org/10.1063/PT.6.1.20220318a

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Publication: [A] Wang, G.*, Phan, T. V.*, Li, S., Wombacher, M., Qu, J., Peng, Y., Chen, G., Goldman, D. I., Levin, S., Austin, R. H., and Liu, L., Emergent Field-Driven Robot Swarm States, Phys. Rev. Lett. 126, 108002 (2021). [*co-first authors]
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.108002

[B] Wang, G.*, Phan, T. V.*, Li, S., Wang, J., Peng, Y., Chen, G., Goldman, D. I., Levin, S. A., Pienta, K., Amend, S., Austin, R. H., and Liu, L., Robots as Models of Evolving Systems, PNAS, 119(12), e2120019119 (2022). [*co-first authors]
https://www.pnas.org/doi/10.1073/pnas.2120019119

[C] Phan, T. V.*, Wang, G.*, Liu, L., and Austin, R. H., Bootstrapped Motion of an Agent on an Adaptive Resource Landscape, Symmetry, 13, 225 (2021). [*co-first authors]
https://www.mdpi.com/2073-8994/13/2/225

Presenters

  • Trung Phan

    • Johns Hopkins University

Authors

  • Trung Phan

    • Johns Hopkins University

  • Gao Wang

    • Chongqing University
    • Wenzhou Institute, University of Chinese Academy of Sciences

  • Shengkai Li

    • Princeton University

  • Daniel I Goldman

    • Georgia Institute of Technology
    • Georgia Tech

  • Simon A Levin

    • Princeton University

  • Robert H Austin

    • Princeton University

  • Liyu Liu

    • Chongqing University