  Self-adaptation is a crucial feature of autonomous systems that must cope
with uncertainties in, e.g., their environment and their internal state.
Self-adaptive systems are often modelled as two-layered systems with a managed
subsystem handling the domain concerns and a managing subsystem implementing
the adaptation logic. We consider a case study of a self-adaptive robotic
system; more concretely, an autonomous underwater vehicle (AUV) used for
pipeline inspection. In this paper, we model and analyse it with the
feature-aware probabilistic model checker ProFeat. The functionalities of the
AUV are modelled in a feature model, capturing the AUV's variability. This
allows us to model the managed subsystem of the AUV as a family of systems,
where each family member corresponds to a valid feature configuration of the
AUV. The managing subsystem of the AUV is modelled as a control layer capable
of dynamically switching between such valid feature configurations, depending
both on environmental and internal conditions. We use this model to analyse
probabilistic reward and safety properties for the AUV.
