Adaptive shape control for microswimmer navigation in turbulence

TL;DR

This paper demonstrates that adaptive shape-changing strategies, learned via reinforcement learning, significantly improve microswimmer navigation in turbulent flows, offering a robust control paradigm for complex environments.

Contribution

It introduces a reinforcement learning-based approach enabling microswimmers to adapt their shape for improved navigation in turbulence, a novel exploration of active morphological control.

Findings

RL-learned strategies outperform fixed-shape baselines

Adaptive shape control remains effective in fully turbulent flows

Proposed analytical model captures key navigation mechanisms

Abstract

Navigation in turbulent environments is a fundamental challenge for biological and artificial microswimmers. While most existing studies focus on adapting motility or steering, the role of active morphological changes in navigation remains poorly explored. Here, we investigate a shape-changing spheroidal microswimmer tasked with maximising its displacement from an initial position in two-dimensional stochastic and turbulent flows. Using reinforcement learning (RL), the microswimmer learns to adapt its aspect ratio based on its orientation and local velocity-gradient signals. The learned strategies outperform fixed-shape and short-time-optimal baselines across different flow regimes and remain effective when transferred from stochastic flows to fully resolved turbulence. Guided by the learned policies, we propose a minimal analytical model that captures the essential navigation mechanisms and reproduces the performance across flow regimes. These results show that adaptive morphology provides a robust and physically interpretable control paradigm for microswimmer navigation in complex flows.