Discovering Antagonists in Networks of Systems: Robot Deployment

TL;DR

This paper presents a novel anomaly detection method for robot swarms using normalizing flows trained on normal behavior data, effectively identifying antagonistic agents with high accuracy in simulations and hardware tests.

Contribution

It introduces a new contextual anomaly detection approach that does not require prior knowledge of anomaly types, improving robustness and accuracy over existing methods.

Findings

Detects at least 80% of antagonistic behaviors with less than 5% false positives.

Validates the method in both simulated and real hardware scenarios.

Outperforms state-of-the-art approaches in detection accuracy and robustness.

Abstract

A contextual anomaly detection method is proposed and applied to the physical motions of a robot swarm executing a coverage task. Using simulations of a swarm's normal behavior, a normalizing flow is trained to predict the likelihood of a robot motion within the current context of its environment. During application, the predicted likelihood of the observed motions is used by a detection criterion that categorizes a robot agent as normal or antagonistic. The proposed method is evaluated on five different strategies of antagonistic behavior. Importantly, only readily available simulated data of normal robot behavior is used for training such that the nature of the anomalies need not be known beforehand. The best detection criterion correctly categorizes at least 80% of each antagonistic type while maintaining a false positive rate of less than 5% for normal robot agents. Additionally, the method is validated in hardware experiments, yielding results similar to the simulated scenarios. Compared to the state-of-the-art approach, both the predictive performance of the normalizing flow and the robustness of the detection criterion are increased.