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.

翻译：使用自主车辆进行化学源本地化是救灾小组安全高效地处理化学紧急情况的关键有利工具。虽然在使用自主系统进行源本地化方面已经做了大量工作,但大多数先前的工程都假设了开放的环境或采用简单的障碍避免办法,这与估算程序是分开的。在本文件中,我们探讨了将源术语估计和在整体框架内避免障碍的路径规划任务结合起来的问题。拟议的系统根据目前对风场和本地地图的估计,明智地产生了潜在的气体取样地点。随后,进行了树木搜索,以产生通往估计源地点的路径,该路径绕着任何障碍绕行,仍然允许探索可能更高级的取样地点。随后,在一系列高忠诚模拟中,对拟议的知情的树规划算法进行了测试。发现拟议的系统比特征丰富环境中的Entrotaxis系统更高效地减少了源位置错误,同时也展示了更加一致和有力的结果。