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.

翻译：自适应是自主系统应对环境及内部状态等不确定性时必须具备的关键特性。这类系统通常被建模为双层架构：被管理层子系统处理领域业务逻辑，管理层子系统实现自适应逻辑。我们以自适应机器人系统为案例研究——具体而言，是用于管道检测的自主水下航行器（AUV）。本文采用特征感知概率模型检测器ProFeat对其进行建模与分析。AUV的功能通过特征模型进行表达，该模型捕获了AUV的可变性。这使得我们能够将被管理子系统建模为一个系统族，其中每个族成员对应AUV的有效特征配置。管理子系统则建模为控制层，能够根据环境与内部条件动态切换这些有效特征配置。基于该模型，我们对AUV的概率奖赏属性与安全性属性进行了分析。