# Distributed Algorithms for Stochastic Source Seeking with Mobile Robot Networks: Technical Report

arXiv: https://arxiv.org/abs/1402.0051 · HTML: https://mlsanity.com/paper/1402.0051/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2014.

## Built on by (most cited first, 2 of 2)

- [Learning to Seek: Multi-Agent Online Source Seeking Against Non-Stochastic Disturbances](https://mlsanity.com/paper/2305.00154/) (2023)
- [Differentially Private Distributed Estimation and Learning](https://mlsanity.com/paper/2306.15865/) (2023)

## Abstract (arXiv)

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow the stochastic gradient of the mutual information between their expected measurements and the location of the source in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal.