| 1. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
- 2019
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:5
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Tidskriftsartikel (refereegranskat)abstract
- Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.
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| 2. |
- Wiedemann, Thomas, 1988-, et al.
(författare)
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Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge
- 2019
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Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 19:3
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Tidskriftsartikel (refereegranskat)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.
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| 3. |
- Wiedemann, Thomas, 1988-, et al.
(författare)
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Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling
- 2017
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Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017). - : IEEE. - 9781509023936 - 9781509023929 ; , s. 122-124
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Konferensbidrag (refereegranskat)abstract
- Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.
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| 4. |
- Wiedemann, Thomas, 1988-
(författare)
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Domain Knowledge Assisted Robotic Exploration and Source Localization
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Deploying mobile robots to explore hazardous environments provides an advantageous way to avoid threats for human operators. For example, in situations, where airborne toxic or explosive material is leaking, robots can be dispatched to localize the leaks. This thesis investigates a novel exploration strategy to automatically localize such emission sources with multiple mobile robots that are equipped with sensors to measure the concentration of the emitted gas.The problem of localizing gas sources consists of two sub-problems that are addressed here. First, the thesis develops a method to estimate the source locations from sequences of localized concentration measurements. This approach can be also applied in case the measurements are collected by static sensor networks or human operators. Second, the thesis proposes an exploration strategy that guides mobile robots to informative measurement locations. With this strategy, a high level of autonomy is achieved and it is ensured that the collected measurements help to estimate the sources. As the main contribution, the proposed approach incorporates prior available domain knowledge about the gas dispersion process and the environment. Accordingly, the approach was coined Domain-knowledge Assisted Robotic Exploration and Source-localization (DARES). Domain knowledge is incorporated in two ways. First, the advection-diffusion Partial Differential Equation (PDE) provides a mathematical model of the gas dispersion process. A Bayesian interpretation of the PDE allows us to estimate the source distribution and to design the exploration strategy. Second, the additional assumption is exploited that the sources are sparsely distributed in the environment, even though we do not know their exact number. The Bayesian inference approach incorporates this assumption by means of a sparsity inducing prior.Simulations and experiments show that the sparsity inducing prior helps to localize the sources based on fewer measurements compared to not exploiting the sparsity assumption. Further, the DARES approach results in efficient measurement patterns of the robots, which tend to start in downwind regions and move in upwind direction towards the sources where they cluster their measurements. It is remarkable that this behavior arises naturally without explicit instructions as a result of including domain knowledge and the proposed exploration strategy.
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| 5. |
- Wiedemann, Thomas, 1988-, et al.
(författare)
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Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization
- 2017
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Ingår i: 2017 European Conference on Mobile Robots (ECMR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538610961 - 9781538610978
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Konferensbidrag (refereegranskat)abstract
- Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.
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