Optimal switching strategies for stochastic geocentric/egocentric navigation

TL;DR

This paper investigates optimal switching strategies between egocentric and geocentric navigation modes in noisy environments, inspired by animal behavior, using a correlated random walk model to improve navigation robustness.

Contribution

It introduces a model for optimal switching between navigation strategies under noise, inspired by dung beetle behavior, and analyzes their robustness and performance.

Findings

Identifies optimal switching schemes for navigation under noise.

Demonstrates robustness of strategies across environmental variations.

Provides insights into animal-inspired navigation algorithms.

Abstract

Animals use a combination of egocentric navigation driven by the internal integration of environmental cues, interspersed with geocentric course correction and reorientation, often with uncertainty in sensory acquisition of information, planning and execution. Inspired directly by observations of dung beetle navigational strategies that show switching between geocentric and egocentric strategies, we consider the question of optimal strategies for the navigation of an agent along a preferred direction in the presence of multiple sources of noise. We address this using a model that takes the form of a correlated random walk at short time scales that is interspersed with reorientation events that yields a biased random walks at long time scales. We identify optimal alternation schemes and characterize their robustness in the context of noisy sensory acquisition, and performance errors linked with variations in environmental conditions and agent-environment interactions.