Motile microorganisms, like bacteria and algae, unify abilities like self-propulsion, autonomous navigation, and decision-making on the micron scale. While recent breakthroughs have led to the creation of synthetic microswimmers and nanoagents that can also self-propel, they still lack the functionality and sophistication of their biological counterparts. This study pioneers a mechanism enabling synthetic agents to autonomously navigate and make decisions, allowing them to solve mazes and transport cargo through complex environments without requiring external cues or guidance. The mechanism exploits chemo-hydrodynamic signals, produced by agents like active droplets or colloids, to remotely sense and respond to their environment - similar to echolocation. Our research paves the way for endowing autonomous, motile synthetic agents with functionalities that have been so far exclusive to biological organisms.

This dataset contains codes that run time-dependent simulations of a synthetic agent producing a chemical field and then responding to it dynamically via chemorepulsion inside maze-shaped domains. Supports moving and static point sources, optional advection, chemotactic response, wall avoidance, and self-propulsion. Writes per-timestep concentration (data/conc_.txt) and particle (data/part_.txt) outputs and can render a trajectory video (data/particle_trajectory.mp4).