Autonomous search of an airborne release in urban environments using informed tree planning

TL;DR

This paper presents an integrated autonomous planning system for chemical source localization in urban environments, combining obstacle avoidance and source estimation through informed tree search, outperforming existing methods in simulation.

Contribution

It introduces a holistic framework coupling path planning with source estimation and obstacle avoidance using informed tree search, advancing autonomous chemical source localization.

Findings

Reduces source position error more efficiently than Entrotaxis.

Demonstrates more consistent and robust results in complex environments.

Outperforms existing methods in high fidelity simulations.

Abstract

The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate to the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in a holistic framework. The proposed system intelligently produces potential gas sampling locations based on the current estimation of the wind field and the local map. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations. The proposed informed tree planning algorithm is then tested against the Entrotaxis technique in a series of high fidelity simulations. The proposed system is found to reduce source position error far more efficiently than Entrotaxis in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.