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 ROS 2-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. @en

Robots are becoming more prevalent in industry and society as a whole. Alongside this growth their application domain is also broadening. Each application brings with it a host of potential uncertainties that the robots should be able to handle at runtime. To tackle this, the doctoral thesis outlined in this paper proposes to address three main problems. First, the current ad-hoc state of robotics software which impedes its evolution. Second, the inability to imagine every possible uncertainty at design time leading to unexpected scenarios at runtime. Third, unexpected scenarios resulting from the reality gap between the simulated environments in which robots are developed versus the real world. These unexpected scenarios may cause a system to violate its requirements, especially in our case non-functional requirements. In an attempt to solve these problems, we plan to implement a variety of self-adaptation strategies. These strategies allow systems to change their composition to handle the afore-mentioned unexpected events during operation autonomously. To accomplish this we will need to reason about how best to integrate these strategies into the software of existing robots, as well as how existing information available to designers regarding the robots can best be utilized to improve the strategies. Lastly, these strategies and the process through which they are integrated will be assessed in their impact across different robotic case studies. Preliminary results from the work towards the thesis are also presented, alongside a consideration of its potential industrial impact.@en