# Ergodic Exploration of Distributed Information

arXiv: https://arxiv.org/abs/1708.09352 · HTML: https://mlsanity.com/paper/1708.09352/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [Maximum diffusion reinforcement learning](https://mlsanity.com/paper/2309.15293/) (2023)
- [Fast Ergodic Search with Kernel Functions](https://mlsanity.com/paper/2403.01536/) (2024)
- [Flow Matching Ergodic Coverage](https://mlsanity.com/paper/2504.17872/) (2025)
- [Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series](https://mlsanity.com/paper/2104.10731/) (2021)
- [Volumetric Ergodic Control](https://mlsanity.com/paper/2511.11533/) (2025)
- [Experimental validation of UAV search and detection system in real wilderness environment](https://mlsanity.com/paper/2502.17372/) (2025)
- [The Rational Selection of Goal Operations and the Integration ofSearch Strategies with Goal-Driven Autonomy](https://mlsanity.com/paper/2201.08883/) (2022)
- [BOIL: Learning Environment Personalized Information](https://mlsanity.com/paper/2604.17137/) (2026)
- [TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction](https://mlsanity.com/paper/2608.02304/) (2026)

## Abstract (arXiv)

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.