Data fusion in automated robotic inspection systems

M. Friedrich, S.G. Pierce, W. Galbraith, G. Hayward

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Teams of small modular inspection vehicles for automated inspection tasks offer the possibility of employing a variety of different NDE inspection methods simultaneously. By synergistically utilising information derived from multiple sources, individual deficiencies and limitations can be partially compensated, leading to a more accurate and precise evaluation of the condition of the engineering structure under test. This paper presents approaches based on fusion of NDE data that have been obtained by a heterogeneous team of small inspection robots which are equipped with payloads for magnetic flux leakage, eddy current and ultrasonic inspection. Any potential uncertainties in individual measurements regarding the location of defects constitute the basis for fusion methods based on statistical and probabilistic algorithms. Images of a two-dimensional test structure have been constructed from data derived from different scans, indicating the positions of detected artificial defects. Applying the Dempster-Shqfer theory of evidence and Bayesian analysis, the confidence level in the accuracy of these images is increased and the uncertainty reduced.

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Data fusion
Robotics
Inspection
Defects
Magnetic flux
Eddy currents
Ultrasonics
Robots
Uncertainty

Keywords

  • data fusion
  • robotics
  • inspection systems

Cite this

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title = "Data fusion in automated robotic inspection systems",
abstract = "Teams of small modular inspection vehicles for automated inspection tasks offer the possibility of employing a variety of different NDE inspection methods simultaneously. By synergistically utilising information derived from multiple sources, individual deficiencies and limitations can be partially compensated, leading to a more accurate and precise evaluation of the condition of the engineering structure under test. This paper presents approaches based on fusion of NDE data that have been obtained by a heterogeneous team of small inspection robots which are equipped with payloads for magnetic flux leakage, eddy current and ultrasonic inspection. Any potential uncertainties in individual measurements regarding the location of defects constitute the basis for fusion methods based on statistical and probabilistic algorithms. Images of a two-dimensional test structure have been constructed from data derived from different scans, indicating the positions of detected artificial defects. Applying the Dempster-Shqfer theory of evidence and Bayesian analysis, the confidence level in the accuracy of these images is increased and the uncertainty reduced.",
keywords = "data fusion, robotics, inspection systems",
author = "M. Friedrich and S.G. Pierce and W. Galbraith and G. Hayward",
year = "2008",
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language = "English",
journal = "Insight: The Journal of the British Institute of Non-Destructive Testing",
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Data fusion in automated robotic inspection systems. / Friedrich, M.; Pierce, S.G.; Galbraith, W.; Hayward, G.

In: Insight: The Journal of the British Institute of Non-Destructive Testing, 2008.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Data fusion in automated robotic inspection systems

AU - Friedrich, M.

AU - Pierce, S.G.

AU - Galbraith, W.

AU - Hayward, G.

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AB - Teams of small modular inspection vehicles for automated inspection tasks offer the possibility of employing a variety of different NDE inspection methods simultaneously. By synergistically utilising information derived from multiple sources, individual deficiencies and limitations can be partially compensated, leading to a more accurate and precise evaluation of the condition of the engineering structure under test. This paper presents approaches based on fusion of NDE data that have been obtained by a heterogeneous team of small inspection robots which are equipped with payloads for magnetic flux leakage, eddy current and ultrasonic inspection. Any potential uncertainties in individual measurements regarding the location of defects constitute the basis for fusion methods based on statistical and probabilistic algorithms. Images of a two-dimensional test structure have been constructed from data derived from different scans, indicating the positions of detected artificial defects. Applying the Dempster-Shqfer theory of evidence and Bayesian analysis, the confidence level in the accuracy of these images is increased and the uncertainty reduced.

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KW - robotics

KW - inspection systems

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