{"id":{"repo_id":"strathclyde","oai_identifier":"oai:strathclyde:w08929935"},"canonical_url":"https://search.dev.ndltd.org/etd/strathclyde/oai:strathclyde:w08929935","repository":{"repo_id":"strathclyde","name":"University of Strathclyde","base_url":"https://stax.strath.ac.uk/catalog/oai"},"display":{"title":"Reconstructing spatially heterogeneous thermal maps using light-based metrology sensors","abstract":"Modern manufacturing increasingly utilises automated systems for component positioning and assembly. Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %.","abstract_html":"Modern manufacturing increasingly utilises automated systems for component positioning and assembly. Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %.","abstract_has_math":false,"creators":["Miller, Ainsley"],"institution":"University of Strathclyde","degree_name":"phd","degree_level":"doctoral-pg","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Forbes, Alistair","Pierce, Stephen G. (Stephen Gareth)","Hughes, Ben","Mulholland, Anthony"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-24T04:50:56Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.48730/svqr-w733"],"render_values":[{"text":"10.48730/svqr-w733","href":"https://doi.org/10.48730/svqr-w733","code":true}]},{"key":"dc:identifier","label":"Identifier","values":["T15505"],"render_values":[{"text":"T15505","href":null,"code":true}]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["201563286"],"render_values":[{"text":"201563286","href":null,"code":true}]}]},"links":{"outbound_url":"https://stax.strath.ac.uk/concern/theses/w08929935","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Forbes, Alistair","Pierce, Stephen G. 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Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %."]},{"key":"dc:description.abstract","label":"Abstract","values":["Modern manufacturing increasingly utilises automated systems for component positioning and assembly. Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %."]},{"key":"dc:title","label":"Title","values":["Reconstructing spatially heterogeneous thermal maps using light-based metrology sensors"]}]}],"canonical_facts":{"dc:contributor.advisor":["Forbes, Alistair","Pierce, Stephen G. (Stephen Gareth)","Hughes, Ben","Mulholland, Anthony"],"dc:creator":["Miller, Ainsley"],"dc:creator.authoridentifier":["201563286"],"dc:date":["2019"],"dc:date.issued":["2019"],"dc:description":["Modern manufacturing increasingly utilises automated systems for component positioning and assembly. Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %."],"dc:description.abstract":["Modern manufacturing increasingly utilises automated systems for component positioning and assembly. Industries are interested in autonomous manufacturing as it can reduce costs and increase productivity. A vital aspect of autonomous precision manufacturing is large volume metrology. One popular approach to large volume or large scale metrology involves using light rays which travel through the air to calculate the position of an object of interest. Optical-based metrology systems like photogrammetry and laser trackers are crucial in improving the accuracy and quality associated with robotic assembly. In an industrial setting these positional measurements are subject to uncertainties which can in many instances be greater than the required tolerances. One source of uncertainty that arises when considering large scale industrial settings is light refraction (bending of the light ray path) due to temperature fluctuations in the air. This thesis will report on the recent work in using light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. The finite element model software COMSOL Multiphysics was used to simulate light ray paths in complex, two dimensional, spatially varying temperature fields. These simulations provided a sense of the typical measurement uncertainties associated with deploying photogrammetry sensors in environments with spatially heterogeneous temperature distributions. Following this, physical experiments were carried out to assess the sensitivity of the Vicon T160 Photogrammetry system. Later chapters look at solving the inverse problem using Voronoi tessellations to spatially parameterise the refractive index map. A Bayesian approach, namely the reversible jump Markov Chain Monte Carlo method (rj-MCMC), is then used as the optimisation method in the inversion. Using the recovered refractive index map led to improvements in discounting the refractive effects by up to 54 % and the uncertainty of this positioning problem was reduced by up to 89 %. Following this, a second method was employed to reduce computational times, improve the sensitivity of the objective function and further reduce the positioning errors of the photogrammetry system. Using this second method, errors in this positioning problem were reduced by up 67 % and the uncertainty was also reduced by up to 89 %."],"dc:identifier":["T15505"],"dc:identifier.doi":["10.48730/svqr-w733"],"dc:identifier.uri":["https://stax.strath.ac.uk/concern/theses/w08929935"],"dc:publisher.department":["Department of Electronic and Electrical Engineering"],"dc:publisher.institution":["University of Strathclyde"],"dc:title":["Reconstructing spatially heterogeneous thermal maps using light-based metrology sensors"],"dc:type.qualificationlevel":["doctoral-pg"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T04:50:56Z"}