Detecting Anomalous Swarming Agents with Graph Signal Processing

TL;DR

This paper introduces a graph signal processing approach to detect anomalous agents in biological and robotic swarms by analyzing their effects on the swarm's graph Fourier structure, enabling effective anomaly detection.

Contribution

It proposes a novel method that models swarm agents as graph signals and uses graph Fourier analysis to identify anomalies, advancing swarm monitoring techniques.

Findings

Anomalous agents significantly alter the graph Fourier spectrum.

The method effectively detects anomalies in simulated swarm scenarios.

Graph signal processing provides a powerful tool for swarm anomaly detection.

Abstract

Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems. In a robotic or biological swarm, anomalous agents (whether malfunctioning or nefarious) behave differently than the normal agents and attempt to hide in the "chaos" of the swarm. By defining a graph structure between agents in a swarm, we can treat the agents' properties as a graph signal and use tools from the field of graph signal processing to understand local and global swarm properties. Here, we leverage this idea to show that anomalous agents can be effectively detected using their impacts on the graph Fourier structure of the swarm.