论文标题
联合约束贝叶斯对计划，指导，控制和州自动驾驶汽车的估计的优化
Joint Constrained Bayesian Optimization of Planning, Guidance, Control, and State Estimation of an Autonomous Underwater Vehicle
论文作者
论文摘要
自动水下车辆（AUV）的指导，导航和控制（GNC）系统的性能在很大程度上取决于其参数的正确调整。我们的目标是在不同的操作场景中自动调整这些参数有关任意高级控制目标。与文献相反，对整个GNC系统进行了整体调整，这在自动水下车辆的背景下是新的。解决优化问题的主要挑战是计算昂贵的目标函数评估，由于不可行的参数化和众多可调参数而导致的模拟崩溃（在我们的情况13中）。通过使用撞车限制的约束贝叶斯优化来应对这些挑战。该方法在用于探索亚冰川湖泊的三重纳米auv倡议中设计的小型微型AUV的GNC系统中证明了这一方法。我们通过调整整体系统来量化可实现的能源消耗的大幅度降低。此外，为不同的功耗功能，稳健性和准确性要求自动生成不同的参数化。例如。如果允许与计划的路径的最大偏差增加约65％，能源消耗可以减少约28％。这显示了基于优化的调整方法的多功能实际适用性。
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.
