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Journal of Robotics
Volume 2012, Article ID 638394, 15 pages
Research Article

Reconstruction of Riser Profiles by an Underwater Robot Using Inertial Navigation

1Biomedical Engineering Program (PEB/COPPE), The Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering, Universidade Federal do Rio de Janeiro, Avenida Horacio Macedo 2030, Bloco H-338, 21941-914 Rio de Janeiro, RJ, Brazil
2Department of Informatics and Automation, Universita Roma Tre, Via della Vasca Navale, 79, I 00146 Roma, Italy
3Subsin Engineering, Rua Beneditinos, 16, 12th floor, 20081-050 Rio de Janeiro, RJ, Brazil

Received 10 October 2011; Revised 11 January 2012; Accepted 15 January 2012

Academic Editor: Jorge Manuel Dias

Copyright © 2012 Luciano Luporini Menegaldo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a kinematic model and an inertial localization system architecture for a riser inspecting robot. The robot scrolls outside the catenary riser, used for underwater petroleum exploration, and is designed to perform several nondestructive tests. It can also be used to reconstruct the riser profile. Here, a realistic simulation model of robot kinematics and its environment is proposed, using different sources of data: oil platform characteristics, riser static configuration, sea currents and waves, vortex-induced vibrations, and instrumentation model. A dynamic finite element model of the riser generates a nominal riser profile. When the robot kinematic model virtually scrolls the simulated riser profile, a robot kinematic pattern is calculated. This pattern feeds error models of a strapdown inertial measurement unit (IMU) and of a depth sensor. A Kalman filter fuses the simulated accelerometers data with simulated external measurements. Along the riser vertical part, the estimated localization error between the simulated nominal and Kalman filter reconstructed robot paths was about 2 m. When the robot model approaches the seabed it assumes a more horizontal trajectory and the localization error increases significantly.