| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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
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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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| 5. |
- Bennetts, Victor Hernandez, 1980-, et al.
(författare)
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Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments
- 2014
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Ingår i: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA). - : IEEE conference proceedings. - 9781479936854 ; , s. 6362-6367
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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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| 6. |
- 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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| 7. |
- 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
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Ingår i: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings. - : IEEE conference proceedings. - 9781509023929 - 9781509023936
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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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| 8. |
- 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.
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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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| 9. |
- 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
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:3
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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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| 10. |
- Fan, Han, 1989-, et al.
(författare)
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Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks
- 2016
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Ingår i: 2016 IEEE SENSORS. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781479982875
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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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