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

• Main Authors: Peter King, Vardy, A, Alexander Forrest
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2018
• Subjects: Special vehicles; navigation; autonomy; AUV; sonar; computer vision; extreme environments; marine robotics; planning; underwater robotics; Canada; Newfoundland
• Online Access: https://figshare.com/articles/journal_contribution/Teach-and-repeat_path_following_for_an_autonomous_underwater_vehicle/22964066

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 per-formed 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.