When autonomous robots are deployed in an industrial setting they are expected to work 24 hours a day, 7 days a week. Therefore, dependability of the robots is crucial. In this paper we present an approach following the model-driven engineering idea that supports dependability in different stages of the live cycle of robots. In particular we present how model-based testing and diagnosis can be used for this goal and how suitable models for these approaches can be obtained. The proposed approach was evaluated in a real industrial use-case showing superior performance compared to the hand-coded solutions used before.

The tasks robots are employed to achieve are becoming increasingly complex, demanding for dependable operation, especially if robots and humans share common space. Unfortunately, for these robots non-determinism is a severe challenge. Malfunctioning hardware, inaccurate sensors, exogenous events and incomplete knowledge lead to inconsistencies in the robot's belief about the world.

The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an action's outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robot's modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.