Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge

Abstract

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.

Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization

“…Thus, the IG algorithm exploits the domain knowledge offered by the model to derive intelligent strategies. Examples of models used in IG include, but are not limited to, Gaussian processes [7], [10], partially observable Markov decision processes [17] and partial differential equations (PDEs) [18], [19]. Model-based IG achieves a superior performance in tasks for which the model accurately describes the process of interest.…”

“…These approaches exploit a mathematical model of the gas dispersion process to derive an exploration strategy [11], [12]. In [18], [19] the authors proposed a probabilistic Bayesian framework that builds a model of the gas concentration and source locations from only a few measurements. In [18] the authors proposed a greedy strategy that drives robots towards neighbouring regions with the highest uncertainty.…”

“…We consider the domain as obstacle free and bounded. We model the gas dispersion process by the advection-diffusion PDE where we restrict ourselves to the steady state [19]. This corresponds to the following equation:…”

“…Here, the goal of IG is to collect a sufficient amount of measurements so that we can accurately estimate the gas concentration f and source distribution u based on an inverse model of the gas dispersion process. This inverse model uses the same probabilistic approach as in [19], which will be explained in Section III-A. In other words, the source localization task is turned into an estimation problem.…”

A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

“…Tracking the weak information released by the releasing sources in nature and human society through the olfactory tracking device can be regarded as the optimization problem of dynamic multiparameter function. Olfactory searchers can be used in areas related to the gas releases, such as toxic gas detection and location [ 1 ], rescue and relief [ 2 ], explosives detection and location [ 3 ] air pollution source tracking [ 4 ], and fire accident [ 5 ].…”