Distributed Information-based Source Seeking

TL;DR

This paper presents a distributed, information-based multi-robot source seeking algorithm that efficiently localizes a source using local measurements, improves convergence speed, and is robust to measurement errors, demonstrated through simulations and physical experiments.

Contribution

It introduces a novel distributed algorithm that maximizes Fisher information for source localization, outperforming traditional methods in speed and robustness.

Findings

Faster convergence compared to traditional algorithms

Robustness to measurement model errors

Successful physical experiments with small ground vehicles

Abstract

In this paper, we design an information-based multi-robot source seeking algorithm where a group of mobile sensors localizes and moves close to a single source using only local range-based measurements. In the algorithm, the mobile sensors perform source identification/localization to estimate the source location; meanwhile, they move to new locations to maximize the Fisher information about the source contained in the sensor measurements. In doing so, they improve the source location estimate and move closer to the source. Our algorithm is superior in convergence speed compared with traditional field climbing algorithms, is flexible in the measurement model and the choice of information metric, and is robust to measurement model errors. Moreover, we provide a fully distributed version of our algorithm, where each sensor decides its own actions and only shares information with its neighbors through a sparse communication network. We perform intensive simulation experiments to test our algorithms on large-scale systems and physical experiments on small ground vehicles with light sensors, demonstrating success in seeking a light source.