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:一旦在现实世界中部署,自主水下车辆(AUV)就无法进行人类监督,但需要作出决定,以适应不稳定和不可预测的环境。为便利对自我适应的AUV进行研究,本文件介绍了SUAV,这是AUV在系统一级进行两层调整的范例,明确区分了应用和自我适应问题。示范项目侧重于由单一AUV进行水下管道检查的任务,该任务作为ROS2系统实施。这一任务必须完成,同时核算推进器故障和不利环境条件等不确定因素。本文讨论了SUAVE如何使用不同的自我适应框架,通过利用Metacontrol框架将AUV行为与不进行自我适应比较的实验就说明了这一点。实验表明,在任务期间使用Metcontrol来调整AUV的操作,根据完成任务或检查管道长度的总体时间衡量,其性能得到改善。</s>