Optimal motility strategies for self-propelled agents to explore porous media

TL;DR

This study compares different motility strategies for self-propelled micro-robots navigating complex porous media, identifying the most effective pattern based on environment characteristics.

Contribution

It introduces a computational framework to evaluate and optimize motility patterns for micro-robots in disordered porous environments, surpassing biologically inspired strategies.

Findings

Basic sensing improves exploration efficiency.

Optimal motility pattern varies with pore size and porosity.

Simple strategies outperform complex biological patterns.

Abstract

Micro-robots for, e.g., biomedical applications, need to be equipped with motility strategies that enable them to navigate through complex environments. Inspired by biological microorganisms we recreate motility patterns such as run-and-reverse, run-and-tumble or run-reverse-flick applied to active rod-like particles in silico. We investigate their capability to efficiently explore disordered porous environments with various porosities and mean pore sizes ranging down to the scale of the active particle. By calculating the effective diffusivity for the different patterns, we can predict the optimal one for each porous sample geometry. We find that providing the agent with very basic sensing and decision making capabilities yields a motility pattern outperforming the biologically inspired patterns for all investigated porous samples.