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The use of autonomous vehicles for chemical source localisation is a keyenabling tool for disaster response teams to safely and efficiently deal withchemical emergencies. Whilst much work has been performed on sourcelocalisation using autonomous systems, most previous works have assumed an openenvironment or employed simplistic obstacle avoidance, separate to theestimation procedure. In this paper, we explore the coupling of the pathplanning task for both source term estimation and obstacle avoidance in aholistic framework. The proposed system intelligently produces potential gassampling locations based on the current estimation of the wind field and thelocal map. Then a tree search is performed to generate paths toward theestimated source location that traverse around any obstacles and still allowfor exploration of potentially superior sampling locations. The proposedinformed tree planning algorithm is then tested against the Entrotaxistechnique in a series of high fidelity simulations. The proposed system isfound to reduce source position error far more efficiently than Entrotaxis in afeature rich environment, whilst also exhibiting vastly more consistent androbust results.

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