Reducing failure rates of robotic systems though inferred invariants monitoring

System monitoring can help to detect abnormalities and avoid failures. Crafting monitors for today's robotic systems, however, can be very difficult due to the systems' inherent complexity. In this work we address this challenge through an approach that automatically infers system invariants and synthesizes those invariants into monitors. The approach is novel in that it derives invariants by observing the messages passed between system nodes and the invariants types are tailored to match the spatial, temporal, and operational attributes of robotic systems. Further, the generated monitor can be seamlessly integrated into systems built on top of publish-subscribe architectures. An application of the technique on a system consisting of a unmanned aerial vehicle (UAV) landing on a moving platform shows that it can significantly reduce the number of crashes in unexpected landing scenarios.