With the advent of Artificial Intelligence and new manufacturing techniques, Autonomous Underwater Vehicles (AUVs) have started to prevail over their manned version in terms of cost efficiency when it comes to accomplish tasks in ocean exploration, offshore platform and ship maintenance or other military missions. As progress has been made over the past years, autonomy remains a topical issue for the untethered AUVs. Drawing its inspiration from nature, this paper aims at minimizing the energy consumed by the device on a specific mission by allowing its shape, parameterized with Bezier curves, to morph throughout time. The framework is restricted to one dimensional trajectories only. A first step consisting of finding the optimal velocity and shapes added mass coefficient in surge as functions of time for a given mission is presented. Then a way of determining the succession of shapes the AUV must take so that it has the right added mass coefficient at any time is proved and used. This last part is made computationally affordable by using a Neural Network instead of a Boundary Element Method to evaluate the hy-drodynamic coefficient in surge of the shape. Outliers detection and elimination are being performed on the training dataset to increase the predictive model reliability and robustness.
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