| 1. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems
- 2014
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Ingår i: Proceedings of the IEEE Sensors Conference 2014. - : IEEE conference proceedings. ; , s. -2049
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Konferensbidrag (refereegranskat)abstract
- This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.
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| 2. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds
- 2014
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 14:9, s. 17331-17352
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.
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| 3. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Gasbot : A Mobile Robotic Platform for Methane Leak Detection and Emission Monitoring
- 2012
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Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotics for Environmental Monitoring (WREM), Vilamoura, Portugal, October 7-12, 2012.
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Konferensbidrag (refereegranskat)abstract
- Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor, along with a novel gas distribution algorithm to generate methane concentration maps of indoor and outdoor exploration areas. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.
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| 4. |
- Hernandez Bennetts, Victor Manuel, 1980-, et al.
(författare)
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Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities
- 2013
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Ingår i: 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. ; , s. 2335-2340
-
Konferensbidrag (refereegranskat)abstract
- Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.
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| 5. |
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| 6. |
- Pomareda, Victor, et al.
(författare)
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Chemical source localization in real environments integrating chemical concentrations in a probabilistic plume mapping approach
- 2013
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Ingår i: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013).
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Konferensbidrag (refereegranskat)abstract
- Chemical plume source localization algorithms can be classified either as reactive plume tracking or gas distribution mapping approaches. Here, we focus on gas distribution mapping methods where the robot does not need to track the plume to find the source and can be used for other tasks. Probabilistic mapping approaches have been previously applied to real-world data successfully; e.g., in the approach proposed by Pang and Farrell. Instead of the quasi-continuous gas measurement values, this algorithm considers events (detections and non-detections) based on whether the sensor response is above or below a threshold to update recursively a source probability grid map; thus, discarding important information. We developed an extension of this event-based approach, integrating chemical concentrations directly instead of binary information. In this work, both algorithms are compared using real-world data obtained from a photo-ionization detector (PID), a non-selective gas sensor, and an anemometer in real environments. We validate simulation results and demonstrate that the concentration-based approach is more accurate in terms of a higher probability at the ground truth source location, a smaller distance between the probability maximum and the source location, and a more peaked probability distribution, measured in terms of the overall entropy.
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| 7. |
- Arain, Muhammad Asif, 1983-, et al.
(författare)
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Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots
- 2015
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Ingår i: 2015 IEEE International Conference on Robotics and Automation (ICRA). - Washington, USA : IEEE. - 9781479969234 ; , s. 3428-3434
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Konferensbidrag (refereegranskat)abstract
- The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.
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| 8. |
- Arain, Muhammad Asif, 1983-
(författare)
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Efficient Remote Gas Inspection with an Autonomous Mobile Robot
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.
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| 9. |
- Arain, Muhammad Asif, 1983-, et al.
(författare)
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Improving Gas Tomography With Mobile Robots : An Evaluation of Sensing Geometries in Complex Environments
- 2017
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Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings. - : IEEE. - 9781509023929 - 9781509023936
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Konferensbidrag (refereegranskat)abstract
- An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.
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| 10. |
- Arain, Muhammad Asif, 1983-, et al.
(författare)
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Sniffing out fugitive methane emissions : autonomous remote gas inspection with a mobile robot
- 2021
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 40:4-5, s. 782-814
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Tidskriftsartikel (refereegranskat)abstract
- 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’’ (ARMEx) 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 anARMExprototype 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.
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| 11. |
- Arain, Muhammad Asif, 1983-, et al.
(författare)
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The Right Direction to Smell : Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography
- 2016
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Ingår i: 2016 IEEE International Conference on Robotics and Automation (ICRA). - New York, USA : IEEE Robotics and Automation Society. ; , s. 4275-4281
-
Konferensbidrag (refereegranskat)abstract
- Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.
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| 12. |
- Asadi, Sahar, 1983-, et al.
(författare)
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Time-dependent gas distribution modelling
- 2017
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 96, s. 157-170
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Tidskriftsartikel (refereegranskat)abstract
- Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.
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| 13. |
- Bennetts, Victor Hernandez, 1980-, et al.
(författare)
-
Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments
- 2014
-
Ingår i: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 6362-6367
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.
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| 14. |
- Burgués, Javier, et al.
(författare)
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3D Gas Distribution with and without Artificial Airflow : An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors
- 2018
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Ingår i: Proceedings. - Basel, Switzerland : MDPI. - 2504-3900. ; 2:13
-
Tidskriftsartikel (refereegranskat)abstract
- Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.
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| 15. |
- Burgues, Javier, et al.
(författare)
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Gas Distribution Mapping and Source Localization Using a 3D Grid of Metal Oxide Semiconductor Sensors
- 2020
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Ingår i: Sensors and actuators. B, Chemical. - : Elsevier. - 0925-4005 .- 1873-3077. ; 304
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 16. |
- Burgués, Javier, et al.
(författare)
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Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
- 2019
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Ingår i: Sensors. - Basel, Switzerland : MDPI. - 1424-8220. ; 19:3
-
Tidskriftsartikel (refereegranskat)abstract
- This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.
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| 17. |
- Fan, Han, 1989-, et al.
(författare)
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A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
- 2018
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Ingår i: Sensors and actuators. B, Chemical. - Amsterda, Netherlands : Elsevier. - 0925-4005 .- 1873-3077. ; 259, s. 183-203
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Tidskriftsartikel (refereegranskat)abstract
- Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.
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| 18. |
- Fan, Han, 1989-, et al.
(författare)
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Improving Gas Dispersal Simulation For Mobile Robot Olfaction : Using Robot-Created Occupancy Maps And Remote Gas Sensors In The Simulation Loop
- 2017
-
Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings. - : IEEE conference proceedings. - 9781509023929 - 9781509023936
-
Konferensbidrag (refereegranskat)abstract
- Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.
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| 19. |
- Fan, Han, 1989-
(författare)
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Robot-aided Gas Sensing for Emergency Responses
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Emergency response personnel can be exposed to various extreme hazards during the response to natural and human-made disasters. In many of the scenarios, one of the risk factors is the presence of hazardous airborne chemicals. Addressing this risk factor requires typical tiring, taxing and toxic operations that are suitable to be aided by Mobile Robot Olfaction (MRO) techniques. MRO is the research domain combining intelligent mobile robots with an artificial sense of smell. It presents the prospect of practical applications for emergency response as it allows to convey useful information on-site and online without risking the safety of human responders. However, standard gas sampling procedures for laboratory use are not directly applicable to MRO due to the complexity of uncontrolled environments and the need for fast deployment and analysis. Besides, state-of-the-art gas sensing approaches have difficulties handling A Priori Unknown Gases (APUG). In APUG situations, the number or/and identities of the present chemicals are unknown, posing challenges in recognizing the underlying risks with conventional solutions such as supervised learning-based electronic noses or dedicated gas sensors targeting known analytes.This dissertation focuses on contributions toward real-world applications of robot-aided gas sensing with an APUG problem setup. The dissertation starts with a requirement analysis of Gas Sensing for Emergency Response (GSER) to identify the key tasks in ad hoc applications. Considering that not all analytes of interest in a field application may be known in advance, a pipeline incorporating non-supervised detection and discrimination of multiple chemicals and consequent distribution modelling is found to be important for GSER. The remainder of the thesis fills this pipeline with three steps: 1) An ensemble learning-based gas detection approach is proposed to recognize significant changes from sensor signals as well as model the baseline response pattern. 2) A clustering analysis-based gas discrimination approach is developed to perform online analysis that automatically learns the number of different chemical compounds from the acquired measurements and provides a probabilistic representation of their class labels. 3) The integration of the proposed non-supervised gas detection and gas discrimination approaches with gas distribution modelling allows prototyping of a GSER system, which can enhance emergency responders’ situational awareness in the target environment. This GSER system demonstrates the concept of discriminating and mapping multiple unknown chemical compounds in uncontrolled environments with validation and evaluation using real-world data sets.During the research on the GSER system, gas dispersal simulation is also investigated to facilitate MRO algorithm development and validation in general. In-field experiments of MRO algorithms are often time-consuming, expensive, cumber some, and lack repeatability, while most of the available simulation tools are limited to insitu gas sensors and simple environments. These issues were addressed by improving a simulation framework to replicate geometrical representations of actual real-world environments and support a variety of gas sensor models. The potential applicability of the resulting work is demonstrated by simulating a gas emission monitoring task and facilitating the development process of a state-of-the-art time-dependent gas distribution modelling algorithm.
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| 20. |
- Fan, Han, 1989-, et al.
(författare)
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Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
- 2019
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Ingår i: 18th ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). - : IEEE.
-
Konferensbidrag (refereegranskat)abstract
- Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.
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| 21. |
- Fan, Han, 1989-, et al.
(författare)
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Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
- 2019
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:3
-
Tidskriftsartikel (refereegranskat)abstract
- Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.
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| 22. |
- Fan, Han, 1989-, et al.
(författare)
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Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks
- 2016
-
Ingår i: 2016 IEEE SENSORS. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479982875
-
Konferensbidrag (refereegranskat)abstract
- Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm.
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| 23. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
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Creating true gas concentration maps in presence of multiple heterogeneous gas sources
- 2012
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Ingår i: Sensors, 2012 IEEE. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781457717666 ; , s. 554-557
-
Konferensbidrag (refereegranskat)abstract
- Gas distribution mapping is a crucial task in emission monitoring and search and rescue applications. A common assumption made by state-of-the art mapping algorithms is that only one type of gaseous substance is present in the environment. For real world applications, this assumption can become very restrictive. In this paper we present an algorithm that creates gas concentration maps in a scenario where multiple heterogeneous gas sources are present. First, using an array of metal oxide (MOX) sensors and a pattern recognition algorithm, the chemical compound is identified. Then, for each chemical compound a gas concentration map using the readings of a Photo Ionization Detector (PID) is created. The proposed approach has been validated in experiments with the sensors mounted on a mobile robot which performed a predefined trajectory in a room where two gas sources emitting respectively ethanol and 2-propanol have been placed.
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| 24. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Integrated Simulation of Gas Dispersion and Mobile Sensing Systems
- 2015
-
Ingår i: Workshop on Realistic, Rapid and Repeatable Robot Simulation.
-
Konferensbidrag (refereegranskat)abstract
- Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations.
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| 25. |
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| 26. |
- Hernandez Bennetts, Victor, et al.
(författare)
-
Mobile robots for localizing gas emission sources on landfill sites : is bio-inspiration the way to go?
- 2012
-
Ingår i: Frontiers in Neuroengineering. - : Frontiers Media S.A.. - 1662-6443. ; 4:20, s. 1-12
-
Tidskriftsartikel (refereegranskat)abstract
- Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully translated into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.
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| 27. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
- 2019
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:5
-
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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| 28. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Online parameter selection for gas distribution mapping
- 2014
-
Ingår i: Sensor Letters. - : American Scientific Publishers. - 1546-198X .- 1546-1971. ; 12:6-7, s. 1147-1151
-
Tidskriftsartikel (refereegranskat)abstract
- The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms.
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| 29. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots
- 2017
-
Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 2:2, s. 1117-1123
-
Tidskriftsartikel (refereegranskat)abstract
- For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback–Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
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| 30. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Robot assisted gas tomography : an alternative approach for the detection of fugitive methane emissions
- 2014
-
Ingår i: Workshop on Robot Monitoring.
-
Konferensbidrag (refereegranskat)abstract
- Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.
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| 31. |
- Hernandez Bennetts, Victor, 1980-, et al.
(författare)
-
Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes
- 2016
-
Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509037629 ; , s. 131-136
-
Konferensbidrag (refereegranskat)abstract
- In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.
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| 32. |
- Ishida, Hiroshi, et al.
(författare)
-
Using Chemical Sensors as 'Noses' for Mobile Robots
- 2016
-
Ingår i: Essentials of Machine Olfaction and Taste. - Singapore : John Wiley & Sons. - 9781118768488 - 9781118768518 ; , s. 219-246
-
Bokkapitel (refereegranskat)abstract
- Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments.
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| 33. |
- Kamarudin, Kamarulzaman, et al.
(författare)
-
Cross-sensitivity of Metal Oxide Gas Sensor to Ambient Temperature and Humidity : Effects on Gas Distribution Mapping
- 2017
-
Ingår i: Proceedings of the 11th Asian Conference on Chemical Sensors. - : American Institute of Physics (AIP). - 9780735414761
-
Konferensbidrag (refereegranskat)abstract
- Metal oxide gas sensors have been widely used in robotics application to perform remote and mobile gas sensing. However, previous researches have indicated that this type of sensor technology is cross-sensitive to environmental temperature and humidity. This paper therefore investigates the effects of these two factors towards gas distribution mapping and gas source localization domains. A mobile robot equipped with TGS2600 gas sensor was deployed to build gas distribution maps of indoor environment, where the temperature and humidity varies. The results from the trials in environment with and without gas source indicated that there is a strong relation between the fluctuation of the mean and variance map with respect to the variations in the temperature and humidity maps.
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| 34. |
- Kamarudin, Kamarulzaman, et al.
(författare)
-
Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization
- 2018
-
Ingår i: Advanced Robotics. - : Taylor & Francis Group. - 0169-1864 .- 1568-5535. ; 32:17, s. 903-917
-
Tidskriftsartikel (refereegranskat)abstract
- Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM+V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5-1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.
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| 35. |
- Khaliq, Ali, 1986-, et al.
(författare)
-
Bringing Artificial Olfaction and Mobile Robotics Closer Together : An Integrated 3D Gas Dispersion Simulator in ROS
- 2015
-
Ingår i: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses.
-
Konferensbidrag (refereegranskat)abstract
- Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.
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| 36. |
- Kucner, Tomasz Piotr, 1988-, et al.
(författare)
-
Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
- 2017
-
Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 2:2, s. 1093-1100
-
Tidskriftsartikel (refereegranskat)abstract
- Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.
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| 37. |
- Kucner, Tomasz, 1988-, et al.
(författare)
-
Tell me about dynamics! : Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models
- 2016
-
Ingår i: Robotics.
-
Konferensbidrag (refereegranskat)abstract
- Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.
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| 38. |
- Louloudi, Athanasia, et al.
(författare)
-
Integration of the humanoid robot Nao inside a smart home : a case study
- 2010
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a case study demonstrating the integration of the humanoid robotic platform Nao within a Network Robot System (NRS) application. The specific scenario of interest takes place in a smart home environment; the task being that of bringing a can of soda from a fridge to a human user. We use this concrete scenario to evaluate how the performance of such a robot can be affected by being embedded inside an intelligent domestic environment. This study points out that, by cooperating with different components on the network the overall performance of the robot is increased.
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| 39. |
- Monroy, Javier, et al.
(författare)
-
GADEN : A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments
- 2017
-
Ingår i: Sensors. - Basel, Switzerland : MPDI AG. - 1424-8220. ; 17:7, s. 1479-1494
-
Tidskriftsartikel (refereegranskat)abstract
- This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.
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| 40. |
|
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| 41. |
- Neumann, Patrick, et al.
(författare)
-
Adaptive gas source localization strategies and gas distribution mapping using a gas-sensitive micro-drone
- 2012
-
Ingår i: Proceedings of the 16th ITG / GMA Conference. - Wunstorf : AMA Service. - 9783981348408 ; , s. 800-809
-
Konferensbidrag (refereegranskat)abstract
- In this paper we exemplify how to address environmental monitoring tasks with a gas-sensitive micro-drone and present two different approaches to locate gas emission sources. First, we sent the micro-drone in real-world experiments along predefined sweeping trajectories to model the gas distribution. The identification of the gas source location is made afterwards based on the created model. Second, we adapted two bio-inspired plume tracking algorithms that have been implemented so far on ground-based mobile robots. We developed a third bio-inspired algorithm, which is called “pseudo gradient-based algorithm”, and compared its perfomance in real-world experiments with the other two algorithms.Keywords: Anemotaxis, chemotaxis, micro UAV, bio-inspired, chemical sensing, gas distribution modeling, gas source localization, gas sensors, mobile sensing system, odor localization, olfaction, plume tracking, quadrocopter.
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| 42. |
- Neumann, Patrick, et al.
(författare)
-
Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms
- 2013
-
Ingår i: Advanced Robotics. - : Informa UK Limited. - 0169-1864 .- 1568-5535. ; 27:9, s. 725-738
-
Tidskriftsartikel (refereegranskat)abstract
- Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion,the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposesan integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-basedplume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitivemicro-drone. We compare the performance of the proposed system in simulations and real-world experiments againsttwo commonly used tracking algorithms adapted for aerial exploration missions.
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| 43. |
- Neumann, Patrick, et al.
(författare)
-
Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs)
- 2013
-
Ingår i: Energy Procedia. - : Elsevier BV. - 1876-6102. ; 37, s. 4182-4190
-
Tidskriftsartikel (refereegranskat)abstract
- Carbon capture & storage (CCS) is one of the most promis ing technologies for greenhouse gas (GHG) management.However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leakdetection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and toincrease the granularity of gas concentration measurements, a quickly deployab le, mobile measurement device isneeded. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHGemissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, theuse of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone.Alternatively, we present an adaptive strategy, which suggests sampling points based on an artific ial potential field(APF). The results of real-world experiments demonstrate the feas ibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.
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| 44. |
- Neumann, Patrick P., et al.
(författare)
-
A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields
- 2014
-
Ingår i: Sensor Letters. - Valencia, USA : American Scientific Publishers. - 1546-198X .- 1546-1971. ; 12:6-7, s. 1113-1118
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can addressenvironmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions,we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate thesuitability of this algorithm for micro UAV platforms.
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| 45. |
- Neumann, Patrick P., et al.
(författare)
-
From Insects to Micro Air Vehicles : A Comparison of Reactive Plume Tracking Strategies
- 2016
-
Ingår i: Intelligent Autonomous Systems 13. - Cham : Springer. - 9783319083384 - 9783319083377 ; , s. 1533-1548
-
Konferensbidrag (refereegranskat)abstract
- Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments.
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| 46. |
- Schaffernicht, Erik, 1980-, et al.
(författare)
-
Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach : The Echo State map approach
- 2017
-
Ingår i: 2017 IEEE International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2659-2665
-
Konferensbidrag (refereegranskat)abstract
- Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.
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| 47. |
- Trincavelli, Marco, et al.
(författare)
-
A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor
- 2012
-
Ingår i: Proceedings of the IEEE Sensors Conference, 2012. - : IEEE Sensors Council. - 9781457717666 ; , s. 550-553
-
Konferensbidrag (refereegranskat)abstract
- Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized.
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| 48. |
- Vuka, Mikel, et al.
(författare)
-
Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot : A Gaussian Regression Bout Amplitude Approach
- 2017
-
Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017). - : IEEE. ; , s. 164-166
-
Konferensbidrag (refereegranskat)abstract
- Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment.
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| 49. |
- 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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| 50. |
- Xing, Yuxin, et al.
(författare)
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FireNose on Mobile Robot in Harsh Environments
- 2019
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 19:24, s. 12418-12431
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Tidskriftsartikel (refereegranskat)abstract
- In this work we present a novel multi-sensor unit, a.k.a. FireNose, to detect and discriminate both known and unknown gases in uncontrolled conditions to aid firefighters under harsh conditions. The unit includes three metal oxide (MOX) gas sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infrared (NDIR) sensor optimized for the detection of CO2, a commercial temperature humidity sensor, and a flow sensor. We developed custom film coatings for the MOX sensors (SnO2, WO3 and NiO) which greatly improved the gas sensitivity, response time and lifetime of the miniature devices. Our proposed system exhibits promising performance for gas sensing in harsh environments, in terms of power consumption (∼ 35 mW at 350°C per MOX sensor), response time (<10 s), robustness and physical size. The sensing unit was evaluated with plumes of gases in both, a laboratory setup on a gas testing rig and on-board a mobile robot operating indoors. These high sensitivity, high-bandwidth sensors, together with online unsupervised gas discrimination algorithms, are able to detect and generate their spatial distribution maps accordingly. In the robotic experiments, the resulting gas distribution maps corresponded well to the actual location of the sources. Therefore, we verified its ability to differentiate gases and generate gas maps in real-world experiments.
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