Gas Source Localization Based on Binary Sensing with a UAV

Wiedemann, Thomas; Schaab, Marius; Gomez, Juan Marchal; Shutin, Dmitriy; Scheibe, Monika; Lilienthal, Achim J.

doi:10.1109/isoen54820.2022.9789553

Cited by 2 publications

(4 citation statements)

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“…Note that p( f k |q, f k−1 ) in essence encodes our process model according to (5). For a perfect model fit, i.e., when (5) holds exactly, we have p( (5)).…”

Section: Bayesian Estimation Of Process Parametersmentioning

confidence: 99%

“…Note that p( f k |q, f k−1 ) in essence encodes our process model according to (5). For a perfect model fit, i.e., when (5) holds exactly, we have p( (5)). We, however, relax (5) as well as the associated boundary condition (4) by allowing stochastic deviations from the equality.…”

Section: Bayesian Estimation Of Process Parametersmentioning

confidence: 99%

“…For a perfect model fit, i.e., when (5) holds exactly, we have p( (5)). We, however, relax (5) as well as the associated boundary condition (4) by allowing stochastic deviations from the equality. Specifically, we use a Gaussian distribution (see also [24]) to model deviations from the exact equalities such that…”

Section: Bayesian Estimation Of Process Parametersmentioning

confidence: 99%

“…Such a system can benefit from the individual platform's high mobility, the robustness to failure of single robots, the possibility of intelligent cooperative data processing approaches, but most importantly from a significantly increased spatial sampling rate of the gas dispersion process, as compared to that achievable by a single surveyor. Various research groups in the field have developed experimental robotic platforms for the purpose of GSL, be that on the lab scale [2][3][4] or as full-scale robotic platforms [5]. While some focus on the robotic aspects alone, many aspire to develop machines that can operate as a swarm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploration and Gas Source Localization in Advection–Diffusion Processes with Potential-Field-Controlled Robotic Swarms

Hinsen,

Wiedemann,

Shutin

et al. 2023

Sensors

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Mobile multi-robot systems are well suited for gas leak localization in challenging environments. They offer inherent advantages such as redundancy, scalability, and resilience to hazardous environments, all while enabling autonomous operation, which is key to efficient swarm exploration. To efficiently localize gas sources using concentration measurements, robots need to seek out informative sampling locations. For this, domain knowledge needs to be incorporated into their exploration strategy. We achieve this by means of partial differential equations incorporated into a probabilistic gas dispersion model that is used to generate a spatial uncertainty map of process parameters. Previously, we presented a potential-field-control approach for navigation based on this map. We build upon this work by considering a more realistic gas dispersion model, now taking into account the mechanism of advection, and dynamics of the gas concentration field. The proposed extension is evaluated through extensive simulations. We find that introducing fluctuations in the wind direction makes source localization a fundamentally harder problem to solve. Nevertheless, the proposed approach can recover the gas source distribution and compete with a systematic sampling strategy. The estimator we present in this work is able to robustly recover source candidates within only a few seconds. Larger swarms are able to reduce total uncertainty faster. Our findings emphasize the applicability and robustness of robotic swarm exploration in dynamic and challenging environments for tasks such as gas source localization.

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“…Note that p( f k |q, f k−1 ) in essence encodes our process model according to (5). For a perfect model fit, i.e., when (5) holds exactly, we have p( (5)).…”

Section: Bayesian Estimation Of Process Parametersmentioning

confidence: 99%