Issue |
EPL
Volume 142, Number 1, April 2023
|
|
---|---|---|
Article Number | 17001 | |
Number of page(s) | 7 | |
Section | Biological and soft matter physics | |
DOI | https://doi.org/10.1209/0295-5075/acc270 | |
Published online | 24 March 2023 |
Optimal active particle navigation meets machine learning(a)
1 Institute of Condensed Matter Physics, Technische Universität Darmstadt - D-64289 Darmstadt, Germany
2 Institut für Theoretische Physik II: Weiche Materie, Heinrich-Heine-Universität Düsseldorf D-40225 Düsseldorf, Germany
(b) E-mail: benno.liebchen@pkm.tu-darmstadt.de (corresponding author)
Received: 14 December 2022
Accepted: 8 March 2023
The question of how “smart” active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve, e.g., chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.
© 2023 The author(s)
Published by the EPLA under the terms of the Creative Commons Attribution 4.0 International License (CC-BY). Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
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