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http://dx.doi.org/10.5762/KAIS.2020.21.8.1

Development of a CNN-based Cross Point Detection Algorithm for an Air Duct Cleaning Robot  

Yi, Sarang (Department of Mechanical Engineering, Kongju National University)
Noh, Eunsol (Department of Mechanical Convergence Engineering, Kongju National University)
Hong, Seokmoo (Department of Mechanical & Automotive Engineering, Kongju National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.8, 2020 , pp. 1-8 More about this Journal
Abstract
Air ducts installed for ventilation inside buildings accumulate contaminants during their service life. Robots are installed to clean the air duct at low cost, but they are still not fully automated and depend on manpower. In this study, an intersection detection algorithm for autonomous driving was applied to an air duct cleaning robot. Autonomous driving of the robot was achieved by calculating the distance and angle between the extracted point and the center point through the intersection detection algorithm from the camera image mounted on the robot. The training data consisted of CAD images of the duct interior as well as the cross-point coordinates and angles between the two boundary lines. The deep learning-based CNN model was applied as a detection algorithm. For training, the cross-point coordinates were obtained from CAD images. The accuracy was determined based on the differences in the actual and predicted areas and distances. A cleaning robot prototype was designed, consisting of a frame, a Raspberry Pi computer, a control unit and a drive unit. The algorithm was validated by video imagery of the robot in operation. The algorithm can be applied to vehicles operating in similar environments.
Keywords
Air Duct; Autonomous; Convolutional Neural Networks; Duct Cleaning; Deep Learning;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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