Teach‐and‐repeat path following for an autonomous underwater vehicle

• Published in: Journal of Field Robotics
• Main Authors: King, Peter, Vardy, Andrew, Forrest, Alexander L
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2018
• Subjects: Life Below Water; extreme environments; marine robotics; planning; underwater robotics; Artificial Intelligence and Image Processing; Electrical and Electronic Engineering; Mechanical Engineering; Industrial Engineering & Automation; Newfoundland
• Online Access: https://escholarship.org/uc/item/4s73v53p; https://escholarship.org/content/qt4s73v53p/qt4s73v53p.pdf; https://doi.org/10.1002/rob.21776

This paper presents a teach-and-repeat path-following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image-generation process, this system exhibits robust image-matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image-matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path-following performance was as desired, with the AUV maintaining close offset to the path.