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

1
German Aerospace Center, 82234 Oberpfaffenhofen, Germany
2
Centre for Applied Autonomous Sensor Systems, Örebro University, 70182 Örebro, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 520; https://doi.org/10.3390/s19030520
Received: 9 December 2018 / Revised: 19 January 2019 / Accepted: 21 January 2019 / Published: 26 January 2019
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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. View Full-Text
Keywords: robotic exploration; gas source localization; mobile robot olfaction; sparse Bayesian learning; multi-agent system; advection-diffusion model robotic exploration; gas source localization; mobile robot olfaction; sparse Bayesian learning; multi-agent system; advection-diffusion model
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Wiedemann, T.; Lilienthal, A.J.; Shutin, D. Analysis of Model Mismatch Effects for a Model-Based Gas Source Localization Strategy Incorporating Advection Knowledge. Sensors 2019, 19, 520.

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