Autonomous Subsea Cable Search and Tracking with Graph-Optimised Priors and Visual Tracking
2026-06-22 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method for underwater robots to find and follow subsea cables, even when the maps showing the cables' locations are uncertain or inaccurate. Their system uses a smart way of updating the cable's route based on real-time camera data and physics models that predict where the cable could physically be. This helps the robot track the cable continuously and quickly recover if it loses sight due to obstacles or errors. They tested their method in real-life trials, where the robot successfully tracked a cable for more than half its length and corrected the initial wrong map information.
subsea cablesautonomous underwater vehiclesgraph-based optimisationcatenary modelssemi-supervised classifiervisual trackingroute uncertaintyreal-time detection
Authors
Ibrahim Fadhil Djauhari, Adrian Bodenmann, Samuel Simmons, Cailei Liang, David White, Susan Gourvenec, Tom Bennetts, Darryl Newborough, Blair Thornton
Abstract
Global communications rely on subsea cable infrastructure that remains vulnerable to damage from natural hazards and human activity. Autonomous underwater vehicles (AUVs) offer an efficient means to inspect long sections of exposed cable, but uncertainty in cable route maps, small cable diameters and partial burial makes continuous tracking a challenge. This paper presents a novel cable search and tracking method that leverages uncertain prior cable route maps. Graph-based optimisation continuously update the cable route to remain consistent with visual observations. Route uncertainty is constrained as a function of distance from observations using physics-based catenary models that account for cable parameters (i.e., lay depth, diameter, and density), bounding the search space to physically feasible regions and improving search efficiency. Cable detection is performed using a semi-supervised classifier running in real-time on-board a camera-equipped AUV. These detections both update the graph-based optimisation and enable visual cable tracking. When tracking is lost due to misclassification, burial or imperfect control, the bounded search space enables efficient recovery. The approach was demonstrated in field trials using the University of Southampton's Smarty200 AUV. The system successfully located the cable despite deliberate errors in it initial cable route map, updating this to be consistent with observations and using visual tracking to inspect up to 59% of a 120m test cable, with successful recovered after tracking loss.