  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.
