Optimal navigation in a noisy environment

TL;DR

This paper demonstrates that simple intermittent course corrections, rather than continuous feedback, enable optimal navigation in noisy environments across biological, physical, and engineered systems, supported by experiments, simulations, and theory.

Contribution

It introduces a universal principle of intermittent course correction for navigation, simplifying strategies and unifying diverse systems under a common framework.

Findings

Optimal correction frequency depends on noise and reorientation costs.

Navigation performance characterized by first-passage time and angular dispersion.

Intermittent corrections outperform continuous feedback in noisy conditions.

Abstract

Navigating toward a known target in a noisy environment is a fundamental problem shared across biological, physical, and engineered systems. Although optimal strategies are often framed in terms of continuous, fine-grained feedback, we show that efficient navigation emerges from a far simpler principle: natural wandering punctuated by intermittent course corrections. Using a controlled robotic platform, active Brownian particle simulations, and scaling theory, we identify a universal trade-off between noise-induced deviation and the finite cost of reorientation, yielding an optimal course correction frequency governed by only a few system parameters. Despite their differing levels of complexity, our experiment and theory collapse onto common quantitative signatures, including first-passage time distribution and non-Gaussian angular dispersion. Our results establish intermittent course-correction as a minimal and robust alternative to continuous feedback, offering a unifying guiding principle for point-to-point navigation in complex environments.