German Aerospace Center, 82234 Oberpfaffenhofen, Germany.
Centre for Applied Autonomous Sensor Systems, Örebro University, 70182 Örebro, Sweden.
Sensors (Basel). 2019 Jan 26;19(3):520. doi: 10.3390/s19030520.
In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.
在有毒物质泄漏的灾难场景中，气体源定位是一项常见但也很危险的任务。为了降低对人类操作人员的威胁，我们提出了一种智能采样策略，使多机器人系统能够基于气体浓度测量自主定位未知气体源。本文讨论了一种基于概率的、基于模型的方法，将物理过程知识纳入采样策略中。我们使用偏微分方程来对气体扩散的时空动态进行建模，该方程考虑了扩散和对流效应。我们假设气体源的数量是未知的，但假定气体源是稀疏分布的。为了纳入稀疏性假设，我们使用了稀疏贝叶斯学习技术。概率建模可以考虑可能的模型失配效应，否则这些效应可能会破坏确定性方法的性能。在本文中，我们使用合成数据在模拟中评估了所提出的气体源定位策略。与实际实验相比，模拟环境为我们提供了必要的真实数据和可重复性，以便更深入地了解所提出的策略。研究表明：(i)概率模型可以补偿不完善的建模；(ii)稀疏性假设可以显著加快源定位速度；(iii)先验的平流知识对源定位有利，但只需要具有一定的准确性。这些发现将有助于在未来为实际应用中参数化所提出的算法。
