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

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.