Ergodic Exploration of Distributed Information

TL;DR

This paper introduces an ergodic control method for autonomous robots that efficiently balances exploration and exploitation by using trajectory ergodicity with respect to an information density map, demonstrated in underwater target localization.

Contribution

The paper proposes a novel ergodic control algorithm for active search that does not require discretization and effectively handles diffuse or localized information in dynamic environments.

Findings

EEDI outperforms traditional information-oriented controllers in complex scenarios.

The method effectively balances exploration and exploitation.

Successful underwater target localization demonstrates practical applicability.

Abstract

This paper presents an active search trajectory synthesis technique for autonomous mobile robots with nonlinear measurements and dynamics. The presented approach uses the ergodicity of a planned trajectory with respect to an expected information density map to close the loop during search. The ergodic control algorithm does not rely on discretization of the search or action spaces, and is well posed for coverage with respect to the expected information density whether the information is diffuse or localized, thus trading off between exploration and exploitation in a single objective function. As a demonstration, we use a robotic electrolocation platform to estimate location and size parameters describing static targets in an underwater environment. Our results demonstrate that the ergodic exploration of distributed information (EEDI) algorithm outperforms commonly used information-oriented controllers, particularly when distractions are present.