Autonomous search for a diffusive source in an unknown environment

TL;DR

This paper introduces a Bayesian-based method for autonomous olfactory search in unknown environments with obstacles, using a particle filter and information-driven control to locate diffusive sources with noisy, sporadic data.

Contribution

It develops a novel integrated approach combining source estimation, environment mapping, and localization within a Bayesian framework for complex, obstacle-rich environments.

Findings

Effective source localization demonstrated in simulations

Robust mapping and localization with noisy measurements

Performance validated through numerical experiments

Abstract

The paper presents an approach to olfactory search for a diffusive emitting source of tracer (e.g. aerosol, gas) in an environment with unknown map of randomly placed and shaped obstacles. The measurements of tracer concentration are sporadic, noisy and without directional information. The search domain is discretised and modelled by a finite two-dimensional lattice. The links is the lattice represent the traversable paths for emitted particles and for the searcher. A missing link in the lattice indicates a blocked paths, due to the walls or obstacles. The searcher must simultaneously estimate the source parameters, the map of the search domain and its own location within the map. The solution is formulated in the sequential Bayesian framework and implemented as a Rao-Blackwellised particle filter with information-driven motion control. The numerical results demonstrate the concept and its performance.