An Experimental Comparison of Anomaly Detection Methods for
Collaborative Robot Manipulators
Abstract
See abstract and datasets
[10.5281/zenodo.5849300]
There exist a large number
of methods that can be used for anomaly detection/fault detection in
collaborative robots. However, studies on these methods tend to only
focus on a single or a couple of such methods, which can make it
challenging to gauge their relative merits in specific robot scenarios.
In this paper, we conduct a comprehensive comparison of 15 methods for
anomaly detection, including methods based on principle component
analysis, local outlier factor, and autoencoders. The methods are
assessed in a typical pick-and-place application with respect to their
capacity to detect a broad range of exogenous anomalies. The results of
the study show that several methods perform well, but that their
performance profiles differ across the studied anomalies. The results
also give an indication of the application characteristics that have the
potential to make anomaly detection challenging.