A pipe-crawling robot using bio-inspired peristaltic locomotion and modular actuated non-destructive evaluation mechanism

In this paper, the design, development, and laboratory testing of a pipe crawling robot for autonomous piping maintenance is presented. The robot consists of a modular design with four cylindrical modules for navigation which uses worm-type locomotion. Two gripping modules at a given sequence alternate gripping action simultaneously to create forward motion along with the other two modules holding the robot in place between gripping sequences. The gripping modules are designed for light-weight, optimized radial traction and to provide the maximum pull force. Then an active inspection mechanism equipped with a computer vision camera is used in the design of a conceptual nondestructive evaluation module. The bio-mimic design of the robot not only provides significant traction with pipe walls to carry NDE equipment, but it also allows conducting multi-scale mechanism tasks. Inspired by peristaltic locomotion, the proposed pipe inspection crawler can perform gripping action using radial motions to adjust to variations of pipes diameter within 4-5 inches inside pipes sloped from 0 to 180 degrees. The initial crawler’s prototype is manufactured using an additive manufacturing process. A laboratory scale test set-up is manufactured for experimentation. Testing performance of the crawler shows that the robot can accomplish horizontal and vertical motions in both upward and downward directions with adjustable gripping force. It also, demonstrated fitting and T-joint compatibility for pipe transitioning.

[1]  Hamad Karki,et al.  Application of robotics in offshore oil and gas industry - A review Part II , 2016, Robotics Auton. Syst..

[2]  Ibrahim N. Tansel,et al.  A non-contact method for part-based process performance monitoring in end milling operations , 2016 .

[3]  I. Tansel,et al.  Non-Contact Quantification of Longitudinal and Circumferential Defects in Pipes using the Surface Response to Excitation (SuRE) Method , 2020, International Journal of Prognostics and Health Management.

[4]  D. G. Harlow,et al.  Tank 241-AY-102 Leak Assessment Supporting Documentation: Miscellaneous Reports, Letters, Memoranda, And Data , 2012 .

[5]  I. Tansel,et al.  Inspection of the Integrity of a Multi-Bolt Robotic Arm Using a Scanning Laser Vibrometer and Implementing the Surface Response to Excitation Method (SuRE) , 2020 .

[6]  Robert J. Wood,et al.  Peristaltic locomotion with antagonistic actuators in soft robotics , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Adrian P. Mouritz,et al.  Review of advanced composite structures for naval ships and submarines , 2001 .

[8]  Ibrahim N. Tansel,et al.  Contact and non-contact approaches in load monitoring applications using surface response to excitation method , 2016 .

[9]  Ibrahim N. Tansel,et al.  A novel approach for classification of loads on plate structures using artificial neural networks , 2016 .

[10]  Hyoukryeol Choi,et al.  Differential-drive in-pipe robot for moving inside urban gas pipelines , 2005, IEEE Transactions on Robotics.

[11]  Vijay Kumar,et al.  The grand challenges of Science Robotics , 2018, Science Robotics.

[12]  S. Hirose,et al.  Design of in-pipe inspection vehicles for /spl phi/25, /spl phi/50, /spl phi/150 pipes , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[13]  M. Dowdall,et al.  The accidental sinking of the nuclear submarine, the Kursk: monitoring of radioactivity and the preliminary assessment of the potential impact of radioactive releases. , 2002, Marine pollution bulletin.

[14]  Damien Chablat,et al.  Dynamic Model of a Bio-Inspired Robot for Piping Inspection , 2019 .

[15]  Hamad Karki,et al.  Application of robotics in onshore oil and gas industry - A review Part I , 2016, Robotics Auton. Syst..

[16]  Roy Burcher,et al.  Concepts in Submarine Design , 1994 .

[17]  Shantanu Datta,et al.  A review on different pipeline fault detection methods , 2016 .

[18]  Matthew Bunn,et al.  Preventing the Next Fukushima , 2011, Science.

[19]  Hongjun Chen,et al.  Tracing and localization system for pipeline robot , 2009 .

[20]  Hadi Fekrmandi,et al.  Reliability of surface response to excitation method for data-driven prognostics using Gaussian process regression , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[21]  H. Fekrmandi,et al.  A data-driven approach of load monitoring on laminated composite plates using support vector machine , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[22]  Hadi Fekrmandi,et al.  Automation of the interpretation of surface response to excitation (SuRE) method by using neural networks , 2015, 2015 7th International Conference on Recent Advances in Space Technologies (RAST).