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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationState EstimationSimilar Papers 제목 키워드 기반
Integrated Guidance and Gimbal Control for Coverage Planning With Visibility Constraints
Coverage path planning with unmanned aerial vehicles (UAVs) is a core task for many services and applications including search and rescue, precision agriculture, infrastructure inspection and surveillance. This work prop…
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic opti…
Stochastic OptimizationConstrained Bayesian Experimental Design via Online Planning
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due …
BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often …
Motion PlanningTransition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexib…
Bayesian Optimization