Once deployed in the real world, autonomous underwater vehicles (AUVs) are out of reach for human supervision yet need to take decisions to adapt to unstable and unpredictable environments. To facilitate research on self-adaptive AUVs, this paper presents SUAVE, an exemplar for two-layered system-level adaptation of AUVs, which clearly separates the application and self-adaptation concerns. The exemplar focuses on a mission for underwater pipeline inspection by a single AUV, implemented as a ROS2-based system. This mission must be completed while simultaneously accounting for uncertainties such as thruster failures and unfavorable environmental conditions. The paper discusses how SUAVE can be used with different self-adaptation frameworks, illustrated by an experiment using the Metacontrol framework to compare AUV behavior with and without self-adaptation. The experiment shows that the use of Metacontrol to adapt the AUV during its mission improves its performance when measured by the overall time taken to complete the mission or the length of the inspected pipeline.

翻译：自主水下航行器（AUV）在真实场景部署后，既无法接受人类监督，又需自主决策以适应不稳定且不可预测的环境。为促进自适应AUV研究，本文提出SUAVE——一种面向AUV双层系统级自适应的示例系统，该方案将应用逻辑与自适应关注点明确分离。该示例聚焦于单AUV执行海底管道检测任务，并基于ROS2系统实现。该任务需在兼顾推进器故障、不利环境条件等不确定性因素的前提下完成。本文探讨了如何将SUAVE应用于不同自适应框架，并通过采用Metacontrol框架开展的对比实验（比较有/无自适应能力的AUV行为）进行说明。实验表明，在AUV执行任务过程中使用Metacontrol进行自适应，能够从完成任务总耗时及检测管道长度两个维度提升其性能。