4.7 Article

Navigation of micro-swimmers in steady flow: the importance of symmetries

Journal

JOURNAL OF FLUID MECHANICS
Volume 932, Issue -, Pages -

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2021.978

Keywords

micro-organism dynamics; active matter; machine learning

Funding

  1. Knut and Alice Wallenberg Foundation [2019.0079]
  2. VR grant [2017-3865]
  3. Vetenskapsradet [2018-03974]
  4. joint China-Sweden mobility programme (National Natural Science Foundation of China (NSFC)-Swedish Foundation for International Cooperation in Research and Higher Education (STINT)) [11911530141, CH2018-7737]
  5. Institute for Guo Qiang of Tsinghua University [2019GQG1012]
  6. NSFC [91752205]
  7. Swedish Research Council [2018-03974] Funding Source: Swedish Research Council

Ask authors/readers for more resources

Marine micro-organisms face challenges in navigation and survival in complex flow patterns, with studies using reinforcement learning to determine optimal strategies. Interestingly, motile micro-organisms primarily rely on local cues for survival decisions rather than accessing global information.
Marine micro-organisms must cope with complex flow patterns and even turbulence as they navigate the ocean. To survive they must avoid predation and find efficient energy sources. A major difficulty in analysing possible survival strategies is that the time series of environmental cues in nonlinear flow is complex and that it depends on the decisions taken by the organism. One way of determining and evaluating optimal strategies is reinforcement learning. In a proof-of-principle study, Colabrese et al. (Phys. Rev. Lett., vol. 118, 2017, 158004) used this method to find out how a micro-swimmer in a vortex flow can navigate towards the surface as quickly as possible, given a fixed swimming speed. The swimmer measured its instantaneous swimming direction and the local flow vorticity in the laboratory frame, and reacted to these cues by swimming either left, right, up or down. However, usually a motile micro-organism measures the local flow rather than global information, and it can only react in relation to the local flow because, in general, it cannot access global information (such as up or down in the laboratory frame). Here we analyse optimal strategies with local signals and actions that do not refer to the laboratory frame. We demonstrate that symmetry breaking is required to find such strategies. Using reinforcement learning, we analyse the emerging strategies for different sets of environmental cues that micro-organisms are known to measure.

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