Nonequilibrium energetics of sensing and actuation by a smart active particle

TL;DR

This paper develops a minimal thermodynamic model of a self-steering active particle, revealing fundamental energetic trade-offs in sensing, actuation, and locomotion, with implications for embodied navigation.

Contribution

It introduces a decomposition of entropy production in active particles, uncovering thermodynamic bounds on navigation performance across different tasks.

Findings

Identifies energetic costs associated with sensing, actuation, and locomotion.

Shows Pareto optimal trade-offs between energy expenditure and navigation accuracy.

Demonstrates thermodynamic constraints are universal across task geometries.

Abstract

Smart active agents must allocate finite energetic resources across distinct functions, yet the underlying thermodynamic trade-offs remain poorly understood. Here, we introduce a minimal model of a self-steering particle with an internal polarity-cue sensor coupled to an external environmental field, decomposing its steady-state entropy production rate into locomotion, actuation, and sensing costs. This separation exposes an energetic bookkeeping structure underlying even the simplest form of embodied navigation. The emergence of Pareto fronts linking energetic expenditure to localisation precision and path-following performance shows that feedback-controlled active motion is constrained by quantitative thermodynamic bounds that persist across distinct task geometries.