Gas Distribution Mapping Research Articles

Super-resolution for Gas Distribution Mapping

Gas Distribution Mapping (GDM) is a valuable tool for monitoring the distribution of gases in a wide range of applications, including environmental monitoring, emergency response, and industrial safety. While GDM is actively researched in the scope of gas-sensitive mobile robots (Mobile Robot Olfaction), there is a potential for broader applications utilizing sensor networks. This study aims to address the lack of deep learning approaches in GDM and explore their potential for improved mapping of gas distributions. In this paper, we introduce Gas Distribution Decoder (GDD), a learning-based GDM method. GDD is a deep neural network for spatial interpolation between sparsely distributed sensor measurements that was trained on an extensive data set of realistic-shaped synthetic gas plumes based on actual airflow measurements. As access to ground truth representations of gas distributions remains a challenge in GDM research, we make our data sets, along with our models, publicly available. We test and compare GDD with state-of-the-art models on synthetic and real-world data. Our findings demonstrate that GDD significantly outperforms existing models, demonstrating a 35% improvement in accuracy on synthetic data when measured using the Root Mean Squared Error over the entire distribution map. Notably, GDD appears to have superior capabilities in reconstructing the edges and characteristic shapes of gas plumes compared to traditional models. These potentials offer new possibilities for more accurate and efficient environmental monitoring, and we hope to inspire other researchers to explore learning-based GDM.

Використання комплексу газогрунтової зйомки та геофізичних досліджень у задачі окреслення території грязьового вулканізму в межах Розвадівської площі Львівської області

The purpose of the research was to substantiate the scientific basis behind the formation of a mud volcano within a specific section of the Precarpathian depression. It also aims to examine the existence of a near-fault shallow gas deposit in the Rozvadiv area and its possible impact on gas-soil surveying and electrometry fields. The relevance of the work is determined by the need to solve the ecological problem of environmental pollution with hydrocarbon gases, as well as to establish and forecast the degree of emissions of the gas-mud mixture. At the same time, the problem of the presence (or absence) of the relationship between the influence of the Rudnyk gas-prospecting area, which is impacted by drilling, and the Rozvadiv deposit mentioned above is solved by comparing the results of gas-soil and geophysical surveys on a significant territory covering the specified areas. The research methodology consists of the following: 1) application of research with different physical justifications (gas-soil survey and electrometry); 2) comparison of the survey results from the same area and the same grid of observations; 3) detection of anomalies within the studied territory and their interpretation from the point of view of the development of mud volcanism. Research results. The information basis of gasometry involves direct measurements of free gas in a observation regular system in the soil environment atmosphere, their statistical characteristics, and calculated indicators of gas contamination. Therefore, the gas distribution maps are informative. The applied research using the method of Earth’s natural pulsed electromagnetic field made it possible to differentiate the area according to the intensity degree of electromagnetic radiation, taking into account the different depths of the sources, and, thus, identify areas of stressed-strain state of rocks and qualify them by the causes. The scientific novelty lies in the following: 1) indicating the absence of connections between the massif impacted by drilling and a natural disaster; 2) specifying the causes of the natural disaster and its substantiated mechanism and classifying the event as a mud volcano. Practical significance. Interpretation of the obtained results makes it possible to determine the causes of the extraordinary geological event in the Rozvadiv area of the Lviv region, classify the event and its consequences as a manifestation of elements of mud volcanism, and establish the absence of a relationship between the specified event and man-made impact (drilling) of the geological environment in the neighbouring areas.

Structurally Aware 3D Gas Distribution Mapping Using Belief Propagation: A Real-Time Algorithm for Robotic Deployment

This paper proposes a new 3D gas distribution mapping technique based on the local message passing of Gaussian belief propagation that is capable of resolving in real time, concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. The gas mapping problem is formulated as a 3D factor graph of Gaussian potentials, the connections of which are conditioned on local occupancy values. The Gaussian belief propagation framework is introduced as the solver and a new hybrid message scheduler is introduced to increase the rate of convergence. The factor graph problem is then redesigned as a dynamically expanding inference task, coupling the information of consecutive gas measurements with local spatial structure obtained by the robot. The proposed algorithm is compared to the state of the art methods in 2D and 3D simulations and is found to resolve distribution maps orders of magnitude quicker than typical direct solvers. The proposed framework is then deployed for the first time onboard a ground robot in a 3D mapping and exploration task. The system is shown to be able to resolve multiple sensor inputs and output high resolution 3D gas distribution maps in a GPS denied cluttered scenario in real time. This online inference of complicated plume structures provides a new layer of contextual information over its 2D counterparts and enables autonomous systems to take advantage of real time estimates to inform potential next best sampling locations.

AWED: Asymmetric Wavelet Encoder–Decoder Framework for Simultaneous Gas Distribution Mapping and Gas Source Localization

AWED: Asymmetric Wavelet Encoder–Decoder Framework for Simultaneous Gas Distribution Mapping and Gas Source Localization

Detection of Volatile Organic Compounds (VOCs) in Indoor Environments Using Nano Quadcopter

The dispersion of chemical gases poses a threat to human health, animals, and the environment. Leaks or accidents during the handling of samples and laboratory materials can result in the uncontrolled release of hazardous or explosive substances. Therefore, it is crucial to monitor gas concentrations in environments where these substances are manipulated. Gas sensor technology has evolved rapidly in recent years, offering increasingly precise and reliable solutions. However, there are still challenges to be overcome, especially when sensors are deployed on unmanned aerial vehicles (UAVs). This article discusses the use of UAVs to locate gas sources and presents real test results using the SGP40 metal oxide semiconductor gas sensor onboard the Crazyflie 2.1 nano quadcopter. The solution proposed in this article uses an odor source identification strategy, employing a gas distribution mapping approach in a three-dimensional environment. The aim of the study was to investigate the feasibility and effectiveness of this approach for detecting gases in areas that are difficult to access or dangerous for humans. The results obtained show that the use of drones equipped with gas sensors is a promising alternative for the detection and monitoring of gas leaks in closed environments.

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot’s control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.

Gas Distribution Mapping and Source Localization: A Mini-Review

Gas Distribution Mapping and Source Localization: A Mini-Review

Improving Gas Distribution Tomography: Evaluation of Algorithms and Fan-Beam Measurement Geometries Indoors

Gas distribution mapping (GDM) is widely used to evaluate the efficiency of environmental control systems and locate pollution sources. In this study, the optimized fan-beam measurement geometries and tomography algorithms were proposed for mapping the gas distributions indoors accurately. The airflow and gas distributions in two different types of rooms were predicted using the validated computational fluid dynamics (CFD) method. Then, the performances of five algorithms, including iterative and Tikhonov regularization methods, were quantitatively compared, and the measurement geometries (i.e., number of optical paths, grid resolution, and laser emitter amount) were investigated. The effect of measurement noise on reconstruction accuracy was also analyzed. The results have shown that the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood expectation maximization (MLEM) algorithms were better than the other algorithms, and their averaged relative root mean square errors (RRMSEs) were at least 39% lower with the iteration residual of 0.0015. The optimum grid resolution and the number of optical paths were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times20$ </tex-math></inline-formula> grid cells and 200 paths, respectively. In addition, the sensitivity of the SART and MLEM algorithms to noise was almost the same, and the addition of the laser emitter could improve the reconstruction accuracy of most cases in this study.

Mobile Robot Gas Source Localization Using SLAM-GDM with a Graphene-Based Gas Sensor

Mobile olfaction is one of the applications of mobile robots. Metal oxide sensors (MOX) are mobile robots’ most popular gas sensors. However, the sensor has drawbacks, such as high-power consumption, high operating temperature, and long recovery time. This research compares a reduced graphene oxide (RGO) sensor with the traditionally used MOX in a mobile robot. The method uses a map created from simultaneous localization and mapping (SLAM) combined with gas distribution mapping (GDM) to draw the gas distribution in the map and locate the gas source. RGO and MOX are tested in the lab for their response to 100 and 300 ppm ethanol. Both sensors’ response and recovery times show that RGO resulted in 56% and 54% faster response times, with 33% and 57% shorter recovery times than MOX. In the experiment, one gas source, 95% ethanol solution, is placed in the lab, and the mobile robot runs through the map in 7 min and 12 min after the source is set, with five repetitions. The results show the average distance error of the predicted source from the actual location was 19.52 cm and 30.28 cm using MOX and 25.24 cm and 30.60 cm using the RGO gas sensor for the 7th and 12th min trials, respectively. The errors show that the predicted gas source location based on MOX is 1.0% (12th min), much closer to the actual site than that predicted with RGO. However, RGO also shows a larger gas sensing area than MOX by 0.35–8.33% based on the binary image of the SLAM-GDM map, which indicates that RGO is much more sensitive than MOX in the trial run. Regarding power consumption, RGO consumes an average of 294.605 mW, 56.33% less than MOX, with an average consumption of 674.565 mW. The experiment shows that RGO can perform as well as MOX in mobile olfaction applications but with lower power consumption and operating temperature.

Gas source localization and mapping with mobile robots: A review

AbstractWithin 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.

Clustering and Informative Path Planning for 3D Gas Distribution Mapping: Algorithms and Performance Evaluation

Chemical gas dispersion can represent a severe threat to human and animal lives, as well as to the environment. Constructing a map of the distribution of gas in a fast and reliable manner is critical to ensure accurate monitoring of at-risk facilities and coordinate targeted and swift rescue missions when an emergency occurs. In recent years, robots have been endowed with gas sensing capabilities, and several algorithms to generate gas maps have been studied, often producing a 2D map. Two of the major drawbacks of these studies are concerned with the fact that the robot’s path is often fixed to a predefined route, and that the tridimensionality of the gas dispersion phenomenon is not captured by the final gas maps. In this letter, we study the effect of a random walk and an adaptive path planning approach based on informative quantities to gas mapping in a 3D environment using a micro aerial vehicle with severe flight time constraints. We also introduce a clustering strategy to enhance the exploration of the environment. The strategies are compared to a lawnmower movement and evaluated against a ground truth map of the environment, both in simulation and with physical experiments, in order to assess which ones are able to provide an accurate gas map, while simultaneously achieving satisfactory coverage of the desired volume under time constraints.

Utilizing a Rapidly Exploring Random Tree for Hazardous Gas Exploration in a Large Unknown Area

The use of robotics olfaction for gas source localization or mapping has become a concern given the issues of terrorism or industrial accidents that may cause damage to the environment. A typical scenario is to send a robot to a place where a dangerous gas leak has just occurred. The robot’s task is to map gas concentrations in the region of interest as effectively as possible. This paper addresses how the robot performs gas exploration in a large and unknown environment. One of the issues that needs to be addressed is the fact that the computation time of the path planning, frontier detection, goal decision making and gas distribution mapping is slower if all cells in the occupancy grid map are involved in a large environment. Consequently, the Rapidly-exploring Random Tree (RRT) algorithm is chosen as the main algorithm. The RRT graph guides the robot’s navigation, utilizes the vertices as goal candidates, gas mean and variance value, and searches for a new frontier. A new strategy is proposed to address the frontier exploration and gas exploitation trade-off. Finally, a Robot Operating System (ROS), Gazebo, and a 3D gas simulator are used to compare the proposed strategy performance with the others in a large outdoor environment.

Gas Distribution Mapping Based on Compressed Sensing

Gas Distribution Mapping Based on Compressed Sensing

Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot

Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our “Autonomous Remote Methane Explorer” () is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated an prototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

Joint estimation of gas and wind maps for fast-response applications

This work addresses 2D gas and wind distribution mapping with a mobile robot for real-time applications. Our proposal seeks to estimate how gases released in the environment are distributed from a set of sparse and uncertain gas-concentration and wind-flow measurements; such that by exploiting the high correlation between these two magnitudes we may extrapolate their value for unexplored areas. Furthermore, because the air currents are completely conditioned by the environment, we assume a priori knowledge of static elements such as walls and obstacles when estimating both distribution maps. In particular, this joint estimation problem is modeled as a multivariate Gaussian Markov random field (GMRF), combining gas and wind observations under a common maximum a posteriori estimation problem. It considers two lattices of cells (a scalar gas-concentration field and a wind vector field) which are correlated following the physical laws of gas dispersal and fluid dynamics. Finally, we report various experiments in which our proposal is compared to other stochastic gas and gas-wind modeling methods under simulation, to evaluate their performance against a computer fluid-dynamics generated ground-truth, as well as under real and uncontrolled conditions.

Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment

ABSTRACTMobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.

Gas distribution mapping for indoor environments based on laser absorption spectroscopy: Development of an improved tomographic algorithm

Gas distribution mapping (GDM) is an important technology for the study of indoor environment, which can be used to evaluate the efficiency of environmental control system and identify pollutant sources. Most recent studies have implemented GDM through contact sensors or a sensor network, which is difficult to calibrate all the sensors and cover the whole space. In this study, we introduced the non-contact tunable diode laser absorption spectroscopy (TDLAS) technology for GDM in the indoor environment. An improved tomographic algorithm, namely Least Square with Tikhonov Regularization (LSTR), was proposed and compared with two available tomographic algorithms using four validated computational fluid dynamics (CFD) simulations. We also analyzed the effects of the laser emitter placements and optical path densities on the concentration field reconstruction quantitatively. The results showed that the LSTR method could reduce the average relative root mean square error (RRMSE) of tomography by 52%, and the laser emitter at the long edge middle (LEM) can achieve better tomographic performance. The degree of the concentration dispersion from the source would mainly impact the tomographic results: when the sector dispersion (SD) value of concentration distribution was about 2.3 times larger, the average RRMSE value would be decreased by about 40%. The intersection matrix with a higher path density achieved a more accurately reconstructed map due to its lower condition number. In addition, the optical path density was suggested to twice the number of grid cells considering the trade-off between scanning time and accuracy.

SLAM-GDM Implementation on Robot Operating System for Gas Source Localization

Gas Distribution Mapping (GDM) can be defined as visualization for the spatial distribution of gas concentration in an unknown indoor environment. Recently, incidents of gas leakage have increased gradually worldwide. This will significantly increase the casualty rate at the workplace. This paper focusses on the implementation of Kernel DM based SLAM- GDM on Robot Operating System (ROS), a novel approach that will be useful in the effort of localizing the gas source during gas leakage inspection. This approach has been conducted successfully by using ROS-architecture implemented system, tested in simulation and on a mobile robot. The result shows that this implementation achieved average accuracy of 0.71m and 0.32m and therefore deemed suitable for gas source location prediction.

Gas source localization accuracy: A comparison between conventional, weighted arithmetic mean and kernel-based gas distribution mapping methods in small indoor area

This work focuses on comparing gas source localization accuracy of three types of gas source localization methods which are the conventional method, weighted-averaging mean method and variance map from DM+V method. Gas source localization accuracy in this work means the distance error between the calculated source location and the actual source location. All methods were implemented offline using the measurements collected from real-time gas source localization experiments that were done beforehand. The dataset consists of gas sensor measurements, and mobile robot’s location in a 3m by 6m test area. The goal of the work is to shine a light on why recent works in the field are more focusing on improving probabilistic and map-based algorithms. This work also shows how the kernel size of kernel-based gas distribution mapping might affect the gas source localization accuracy in a small indoor area.

Gas distribution mapping and source localization using a 3D grid of metal oxide semiconductor sensors

The difficulty to obtain ground truth (i.e. empirical evidence) about how a gas disperses in an environment is one of the major hurdles in the field of mobile robotic olfaction (MRO), impairing our ability to develop efficient gas source localization strategies and to validate gas distribution maps produced by autonomous mobile robots. Previous ground truth measurements of gas dispersion have been mostly based on expensive tracer optical methods or 2D chemical sensor grids deployed only at ground level. With the ever-increasing trend towards gas-sensitive aerial robots, 3D measurements of gas dispersion become necessary to characterize the environment these platforms can explore. This paper presents ten different experiments performed with a 3D grid of 27 metal oxide semiconductor (MOX) sensors to visualize the temporal evolution of gas distribution produced by an evaporating ethanol source placed at different locations in an office room, including variations in height, release rate and air flow. We also studied which features of the MOX sensor signals are optimal for predicting the source location, considering different lengths of the measurement window. We found strongly time-varying and counter-intuitive gas distribution patterns that disprove some assumptions commonly held in the MRO field, such as that heavy gases disperse along ground level. Correspondingly, ground-level gas distributions were rarely useful for localizing the gas source and elevated measurements were much more informative. We make the dataset and the code publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.