Only released in EOL distros:   kinetic

Overview

The ROS Anomaly Detector Module (ADM) is designed to execute alongside industrial robotic arm tasks to detect unintended deviations at the application level. The ADM utilizes a learning based technique to achieve this. The process has been made efficient by building the ADM as a ROS package that aptly fits in the ROS ecosystem. While this module is specific to anomaly detection for an industrial arm, it is extensible to other projects with similar goals. This is meant to be a starting point for other such projects.

The crux of anomaly detection within this module relies on a three step process. The steps includes creating datasets out of the published messages within ROS, training learning models from those datasets, and deploying it in production. Appropriately, the three modes that the ROS ADM can be executed in are Collect, Learn, and Operate. The following image presents the workflow.