Machining-based coverage path planning for automated structural inspection

Research output: Contribution to journalArticle

Abstract

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets.
LanguageEnglish
Pages202-213
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume15
Issue number1
Early online date19 Sep 2016
DOIs
Publication statusPublished - 1 Jan 2018

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Motion planning
Machining
Inspection
Robotics
Computer aided manufacturing
Drawing (graphics)
Collision avoidance
Carbon steel
Kinematics
Automation
Corrosion
Hardware
Defects
Steel

Keywords

  • inspection
  • robot sensing systems
  • path planning
  • service robots
  • crawlers
  • imaging

Cite this

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title = "Machining-based coverage path planning for automated structural inspection",
abstract = "The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m² carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets.",
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Machining-based coverage path planning for automated structural inspection. / MacLeod, Charles Norman; Dobie, Gordon; Pierce, Stephen Gareth; Summan, Rahul; Morozov, Maxim.

In: IEEE Transactions on Automation Science and Engineering, Vol. 15, No. 1, 01.01.2018, p. 202-213.

Research output: Contribution to journalArticle

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