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

TL;DR

This paper introduces distributed control algorithms for mobile robot networks to localize environmental signal sources, handling both model-based and model-free scenarios with proven robustness and effectiveness demonstrated through simulations.

Contribution

It presents novel distributed algorithms for source localization in robot networks, applicable with or without a known signal model, and guarantees convergence to a local maximum.

Findings

Algorithms successfully localize sources in simulations

Distributed approach is robust to group deformations

Effective in both model-based and model-free scenarios

Abstract

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.