Monitoring Autonomous Agents in Self-Organizing Industrial Systems

In this paper, we extend an existing approach for monitoring work-pieces in Self-Organizing Industrial Systems (SOIS) to be applicable to more complex SOIS settings, where autonomous agents can move and regulate their velocity completely free. The problem of having infeasible many possible valid routes is handled by extracting significant sub-paths (pathlets), by which valid routes can be reconstructed. We show that meaningful pathlets can also be extracted for complex SOIS settings. By adding a temporal component to the dictionary, we are able to detect anomalous trajectories regarding spatial movement and temporal flow. Furthermore, environmental changes, like added/removed machines, can be located using information gained from the reconstruction process. Additionally, we show that the global movement behavior of the agents can be analyzed, too, by monitoring specific movement parameters for the dictionary pathlets. The data for the experiments are generated using a SOIS simulation which implements a state-of-the-art architecture for multi-agent systems.