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所需的关键特性。自适应系统通常被建模为双层系统：下层为处理领域关注点的被管理子系统，上层为实施自适应逻辑的管理子系统。本文以自适应机器人系统为案例开展研究，具体涉及用于管道检测的自主水下航行器(AUV)。我们采用特征感知概率模型检测器ProFeat对其进行建模与分析。通过特征模型对AUV的功能进行建模，捕获其可变性。这使得我们能够将被管理子系统建模为系统家族，其中每个家族成员对应AUV的一个有效特征配置。AUV的管理子系统被建模为控制层，可根据环境和内部条件在有效特征配置间动态切换。基于该模型，我们分析了AUV的概率回报属性与安全性属性。