Autonomous search of an airborne release in urban environments using informed tree planning
This work addresses the problem of safely and efficiently locating chemical releases for disaster response teams, representing an incremental improvement by integrating obstacle avoidance with estimation in complex environments.
The paper tackles the problem of autonomous chemical source localization in urban environments by coupling path planning for both source estimation and obstacle avoidance in a holistic framework. The result is a reduction in source position error more efficiently than the Entrotaxis technique in simulations, with more consistent and robust performance.
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.