Optimal active particle navigation meets machine learning

• URL: http://arxiv.org/abs/2303.05558v1
• Date: Thu, 9 Mar 2023 19:48:03 GMT
• Title: Optimal active particle navigation meets machine learning
• Authors: Mahdi Nasiri, Hartmut L\"owen, Benno Liebchen
• Abstract summary: "Smart" active agents, like colloidal insects, microorganisms, or future 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. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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