Informative Path Planning for Gas Distribution Mapping in Cluttered Environments

Rhodes, Callum; Liu, Cunjia; Chen, Wenhua

doi:10.1109/iros45743.2020.9341781

Cited by 17 publications

(14 citation statements)

References 17 publications

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“…GDM is a task of creating a map of the distribution of an airborne chemical over an area of interest through gathering spatially distributed chemical measurements. The mobile robot can only sample so many points within an acceptable time frame, and so the sparse measurements along with fluctuations in chemical concentration make this a difficult task (Rhodes et al, 2020). In comparison to chemical localization algorithms which attempt to navigate to the chemical source location, GDM algorithms often require the robot to follow a predetermined trajectory whilst periodically gathering concentration measurements.…”

Section: Gas Distribution Mapping (Gdm) With Mobile Robotsmentioning

confidence: 99%

Gas source localization and mapping with mobile robots: A review

Francis

Griffiths

et al. 2022

Journal of Field Robotics

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Within the last couple of decades, there has been increasing research into the use of mobile robots for gas sensing applications. This has led to many different branches of research, most notably on the topics of gas distribution mapping and source localization. This paper aims to provide an up to date and comprehensive overview of the research on these topics for both controlled and uncontrolled environments with ground‐based or aerial robots. To complement other review papers, a greater focus is placed on recent probabilistic algorithms developed for both single robot and multirobot applications.

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Section: Gas Distribution Mapping (Gdm) With Mobile Robotsmentioning

confidence: 99%

Gas source localization and mapping with mobile robots: A review

Francis

Griffiths

et al. 2022

Journal of Field Robotics

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“…Note that the framework proposed in this paper can use both model types (e.g. (Rhodes et al, 2020)), therefore leaving flexibility in the system. However, a parametric model is used in this work, not only due to its computational efficiency and wide spread adoption in the literature, but also because one of the motivations of this work is to show that such simple ATD models are adequate to inform robotic source localisation in urban environments, given features can be accommodated in the path planning algorithm.…”

Section: Estimationmentioning

confidence: 99%

Autonomous search of an airborne release in urban environments using informed tree planning

et al. 2022

Self Cite

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The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

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“…While the above path-planning approaches suggest mapping policies for estimating gas distribution in obstacle-free environments, there is a need for path planning in cluttered areas—in which the risk for humans is greater. Attempting to overcome this problem, the Gaussian Markov Random Field approach maps the task zone as a factor graph, connecting the safe path edges to produce a path that is free from obstacles [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

BREEZE—Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles

Elmakis

Shaked

Fishbain

et al. 2022

Sensors

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Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent.

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Informative Path Planning for Gas Distribution Mapping in Cluttered Environments

Cited by 17 publications

References 17 publications

Gas source localization and mapping with mobile robots: A review

Gas source localization and mapping with mobile robots: A review

Autonomous search of an airborne release in urban environments using informed tree planning

BREEZE—Boundary Red Emission Zone Estimation Using Unmanned Aerial Vehicles

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