Discovering antagonists in networks of systems: Robot deployment
Year: 2025
Type: article
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.
Source: Robotics and Autonomous Systems
Authors: Ingeborg Wenger, Peter Eberhard, Henrik Ebel
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,140
Funder: Deutsche Forschungsgemeinschaft
Awards: G2336861879, G4217943712, Mehr Intelligenz wagen Designassistenten in Mechanik und Dynamik (SPP 2353) Koordinationsantrag, Untersuchung der Mehrkörperdynamik und Regelung für den kollaborativen Transport elastischer Objekte mithilfe eines heterogenen Schwarms von fliegenden und fahrenden mobilen Robotern, G8570497483, EXC 2075: Datenintegrierte Simulationswissenschaft (SimTech)
OpenAlex ID: W4415823989