Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization

Wiedemann, Thomas; Manss, Christoph; Shutin, Dmitriy; Lilienthal, Achim J.; Karolj, Valentina; Viseras, Alberto

doi:10.1109/ecmr.2017.8098707

Cited by 12 publications

(20 citation statements)

References 14 publications

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“…Last but not least, in the future, we will apply the proposed algorithm to real world experiments. For this purpose we already started to develop a suitable robotic platform and tested a similar algorithm in hardware-in-the-loop experiments [43]. In the presented article, we incorporated wind information as a given a-priori known wind field map.…”

Section: Discussionmentioning

confidence: 99%

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

Wiedemann

Lilienthal

Shutin

2019

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Wiedemann

Lilienthal

Shutin

2019

Sensors

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“…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.…”

Section: A Robotic Information Gathering With Reinforcement Learningmentioning

confidence: 99%

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

et al. 2021

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Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks.

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“…This result motivates extension of the Bayesian estimation approach to handle real outdoor environments and deployment on an aerial platform. The use of multiple robots has also been proposed, employing an uncertainty driven exploration strategy to plan their path whilst estimating the locations and intensities of multiple sources (Wiedemann et al, 2017). The method was tested with real robots in hardware in the loop simulations using simulated gas sensor data.…”

Section: Source Term Estimationmentioning

confidence: 99%

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

Hutchinson

Liu

Chen

2018

Journal of Field Robotics

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Gaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

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Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization

Cited by 12 publications

References 14 publications

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

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

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

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

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