Learning a probabilistic self-awareness model for robotic systems

In order to address the problem of failure detection in the robotics domain, we present in this contribution a so-called self-awareness model, based on the system´s internal data exchange and the inherent dynamics of inter-component communication. The model is strongly data driven and provides an anomaly detector for robotics systems both applicable in-situ at runtime as well as a-posteriori in post-mortem analysis. Current architectures or methods for failure detection in autonomous robots are either implementations of watch dog concepts or are based on excessive amounts of domain-specific error detection code. The approach presented in this contribution provides an avenue for the detection of more subtle anomalies originating from external sources such as the environment itself or system failures such as resource starvation. Additionally, developers are alleviated from explicitly modeling and foreseeing every exceptional situation, instead training the presented probabilistic model with the known normal modes within the specification of the robot system. As we developed and evaluated the self-awareness model on a mobile robot platform featuring an event-driven software architecture, the presented method can easily be applied in other current robotics software architectures.