Development of an adaptive in-pipe inspection robot with rust detection and localization

• Main Authors: Diaz, Julianne Alyson I., Ligeralde, Manuel I., Antonio, Micah Antoinette B., Mascardo, Philix Anton R., Maningo, Jose Martin Z., Fernando, Arvin H., Vicerra, Ryan Rhay P., Dadios, Elmer P., Bandala, Argel A.
• Format: text
• Published: Animo Repository 2019
• Subjects: Pipelines—Corrosion; Pipelines—Maintenance and repair; Mobile robots; Electrical and Computer Engineering; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1389; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2388/type/native/viewcontent
• Institution: De La Salle University

In response to addressing the issue of pipe quality checking, the researchers developed an adaptive in-pipe inspection robot that is able to detect rust as well as map the rust on the pipe network. The robot is traversed in a pipe network of horizontal, vertical, elbow, and tee type with diameters of 8, 10 and 12 inches for all. Hence, the test features the versatility, adaptability, and robustness of the robot. The leg expansion of the robot is inspired by the scissors mechanism. On the other hand, rust detection was done through a per pixel classification via image processing. To effectively map the rust, checkpoints were used as a guide of the robot. Testing of the robot were supported in both simulation and actual testing, wherein it yields a 96.45% success rate on the site. Likewise, its rust detection program proved to be successful with a high percentage accuracy of 99.18%. The localization on the other hand yielded an accuracy of 85%. Given the obtained data and results, the researchers were able to go beyond their target objective of 70%. © 2018 IEEE.