Information Swimmer: A Novel Mechanism of Self-propulsion

TL;DR

This paper introduces an information-based self-propulsion mechanism where a swimmer uses velocity measurements and reversible adjustments to move without external energy, linking thermodynamics, information theory, and motion.

Contribution

It proposes a novel information-driven self-propulsion mechanism that operates without external energy input, respecting thermodynamic laws and optimizing control parameters for steady motion.

Findings

Information swimmers can achieve steady velocities comparable to Brownian particles.

Efficiency depends on the noise characteristics of the environment.

Measurement frequency can be reduced in correlated noise environments without loss of efficiency.

Abstract

We study an information-based mechanism of self-propulsion in noisy environment. An information swimmer maintains directional motion by periodically measuring its velocity and accordingly adjusting its friction coefficient. Assuming that the measurement and adjustment are reversible and hence cause no energy dissipation, an information swimmer may move without external energy input. There is however no violation of the second law of thermodynamics, because the information entropy stored in the memory of swimmer increases monotonically. By optimizing its control parameters, the swimmer can achieve a steady velocity that is comparable to the root-mean-square velocity of an analogous Brownian particle. We also define a swimming efficiency in terms of information entropy production rate, and find that in equilibrium media with white noises, information swimmers are generally less efficient than Brownian particles driven by constant forces. For colored noises with long correlation times, the frequency of measurement can be greatly reduced without affecting the efficiency of information swimmers.