Efficient Seafloor Classification and Submarine Cable Route Design Using an Autonomous Underwater Vehicle

This work presents an efficient method for designing submarine cable routes using online seafloor classification based on sidescan sonar scanlines, collected by an autonomous underwater vehicle (AUV). Currently, the cable routes are designed manually by experienced experts. Online seafloor classification and an automated route planning method using an AUV can improve the efficiency of construction of submarine cables. Sidescan sonar is a device commonly used for mapping the seafloor and detecting obstacles. An AUV equipped with a sidescan sonar is used to gather sonar scanlines to perform online seafloor classification and mapping. As the AUV operates, the gathered scanlines are analyzed to classify the seafloor using a probabilistic classifier based on Bayes' theorem with the naïve assumption. Based on the seafloor classification map, a probabilistic roadmap is generated. Then, an A* algorithm is applied to determine appropriate cable routes along the cable corridor, using the following five factors: seafloor classification, bathymetry, steepness of slope, angle of change of course, and cable length. Compared with an existing route planning method, the presented method of using an AUV with automatic decision making algorithm can efficiently support submarine cable construction. The favored route determined by the presented method was as acceptable as the route designed by experts.

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