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The performance of a guidance, navigation and control (GNC) system of anautonomous underwater vehicle (AUV) heavily depends on the correct tuning ofits parameters. Our objective is to automatically tune these parameters withrespect to arbitrary high-level control objectives within different operationalscenarios. In contrast to literature, an overall tuning is performed for theentire GNC system, which is new in the context of autonomous underwatervehicles. The main challenges in solving the optimization problem arecomputationally expensive objective function evaluations, crashing simulationsdue to infeasible parametrization and the numerous tunable parameters (in ourcase 13). These challenges are met by using constrained Bayesian optimizationwith crash constraints. The method is demonstrated in simulation on a GNCsystem of an underactuated miniature AUV designed within the TRIPLE-nanoAUVinitiative for exploration of sub-glacial lakes. We quantify the substantialreduction in energy consumption achieved by tuning the overall system.Furthermore, different parametrizations are automatically generated fordifferent power consumption functions, robustness, and accuracy requirements.E.g. energy consumption can be reduced by ~28%, if the maximum alloweddeviation from the planned path is increased by ~65%. This shows the versatilepractical applicability of the optimization-based tuning approach.

Joint Constrained Bayesian Optimization of Planning, Guidance, Control, and State Estimation of an Autonomous Underwater Vehicle