Improved autonomy in robotic systems is needed for innovation in, e.g., the marine sector. Autonomous robots that are let loose in hazardous environments, such as underwater, need to handle uncertainties that stem from both their environment and internal state. While self-adaptation is crucial to cope with these uncertainties, bad decisions may cause the robot to get lost or even to cause severe environmental damage. Autonomous, self-adaptive robots that operate in uncontrolled environments full of uncertainties need to be reliable! Since these uncertainties are hard to replicate in test deployments, we need methods to formally analyse self-adaptive robots operating in uncontrolled environments. In this article, we show how feature-oriented techniques can be used to formally model and analyse self-adaptive robotic systems in the presence of such uncertainties. Self-adaptive systems can be organised 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 an Autonomous Underwater Vehicle (AUV) for pipeline inspection, in which the managed subsystem of the AUV is modelled as a family of systems, where each family member corresponds to a valid configuration of the AUV which can be seen as an operating mode of the AUV’s behaviour. The managing subsystem of the AUV is modelled as a control layer that is capable of dynamically switching between such valid configurations, depending on both environmental and internal uncertainties. These uncertainties are captured in a probabilistic and highly configurable model. Our modelling approach allows us to exploit powerful formal methods for feature-oriented systems, which we illustrate by analysing safety properties, energy consumption, and multi-objective properties, as well as performing parameter synthesis to analyse to what extent environmental conditions affect the AUV. The case study is realised in the probabilistic feature-oriented modelling language and verification tool ProFeat, and in particular exploits family-based probabilistic and parametric model checking.

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 (SASs) can be realised as two-layered systems, introducing a separation of concerns between the domain-specific functionalities of the system (the managed subsystem) and the adaptation logic (the managing subsystem), i.e., introducing an external feedback loop for managing adaptation in the system. We present an approach to model SASs as dynamic software product lines (SPLs) and leverage existing approaches to SPL-based analysis for the analysis of SASs. To do so, the functionalities of the SAS are modelled in a feature model, capturing the SAS’s variability. This allows us to model the managed subsystem of the SAS as a family of systems, where each family member corresponds to a valid feature configuration of the SAS. Thus, the managed subsystem of an SAS is modelled as an SPL model; more precisely, a probabilistic featured transition system. The managing subsystem of an SAS is modelled as a control layer capable of dynamically switching between these valid configurations, depending on both environmental and internal conditions. We demonstrate the approach on a small-scale evaluation of a self-adaptive autonomous underwater vehicle used for pipeline inspection, which we model and analyse with the feature-aware probabilistic model checker ProFeat. The approach allows us to analyse probabilistic reward and safety properties for the SAS, as well as the correctness of its adaptation logic. • Dynamic software product lines used to model self-adaptive systems. • Family-based analysis used for formal verification of self-adaptive systems. • A case study from the underwater robotics domain to exemplify the approach. • Maintaining separation of concerns between the two layers of a self-adaptive system.

Self-adaptation, meant to increase reliability, is a crucial feature of cyber-physical systems operating in uncertain physical environments. Ensuring safety properties of self-adaptive systems is of utter importance, especially when operating in remote environments where communication with a human operator is limited, like under water or in space. This paper presents a software model that allows the analysis of one such self-adaptive system, a configurable underwater robot used for pipeline inspection, by means of the probabilistic model checker ProFeat. Furthermore, it shows that the configurable software model is easily extensible to further, possibly more complex use cases and analyses.

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.