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Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing, and environmental factors such as people flocking around robots, there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a reliable position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still correct. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization. These patterns are then combined with weak classifiers from the particle set and sensor perception for boosted learning of a localization estimator. Through the extraction of features generated by neural networks and its usage for training strong classifiers, the robots localization accuracy can be estimated. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles. Results show that it is possible to monitor the robots localization accuracy using convolutional as well as recurrent neural networks. The additional boosting using Adaboost also yields an increase in training accuracy. Thus, this paper directly contributes to the verification of localization performance.

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 modeldriven 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.

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