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- 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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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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| 3. |
- 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
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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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| 4. |
- 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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