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

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.Keywords source term estimation • path planning • environmental sampling • autonomous search • informed tree

Gas source localization and mapping with mobile robots: A review

“…A limitation of current probabilistic algorithms can be found in the ATD models that they use, which often do not fully represent the real plume dispersal, especially in cluttered environments. There have however been attempts to consider the effect of obstacles on plume dispersal by combining path planning strategies with probabilistic GSL algorithms (Rhodes et al, 2021). However, there are few examples where such algorithms have been evaluated on real robots.…”

“…More recently,Rhodes et al (2021) proposed a 2D STE search strategy using Informed Tree Planning (ITP) to generate obstacle free trajectories between the robot and source location using a known map of the search space. Considering how obstacles affect the distribution of the plume, Dijkstra's search was used to produce a sample distribution where regions behind obstacles are less likely to contain chemical information due to the blocking effect on the particle's flow within the wind direction(Bellingham et al, 2002).…”