  The performance of a guidance, navigation and control (GNC) system of an
autonomous underwater vehicle (AUV) heavily depends on the correct tuning of
its parameters. Our objective is to automatically tune these parameters with
respect to arbitrary high-level control objectives within different operational
scenarios. In contrast to literature, an overall tuning is performed for the
entire GNC system, which is new in the context of autonomous underwater
vehicles. The main challenges in solving the optimization problem are
computationally expensive objective function evaluations, crashing simulations
due to infeasible parametrization and the numerous tunable parameters (in our
case 13). These challenges are met by using constrained Bayesian optimization
with crash constraints. The method is demonstrated in simulation on a GNC
system of an underactuated miniature AUV designed within the TRIPLE-nanoAUV
initiative for exploration of sub-glacial lakes. We quantify the substantial
reduction in energy consumption achieved by tuning the overall system.
Furthermore, different parametrizations are automatically generated for
different power consumption functions, robustness, and accuracy requirements.
E.g. energy consumption can be reduced by ~28%, if the maximum allowed
deviation from the planned path is increased by ~65%. This shows the versatile
practical applicability of the optimization-based tuning approach.
