On Anomaly Management in Mobile Robot Systems in Intralogistics
Natalia Ogorelysheva, Till Schallau, Dominik Schmid, Mario Günzel, Falk Maria Howar, Jian-Jia Chen, Julia Freytag, J. S. Emmerich · 2025
In this paper, we investigate autonomous intralogistics systems in the context of anomaly management, emphasizing that true autonomy must inherently include the capability for independent decision-making, even when faced with various anomalies. Growing interest in autonomous intralogistics systems, particularly mobile robot-based solutions known for their flexibility, requires effective management of abnormal behaviors. However, in the existing literature, a comprehensive approach that addresses all aspects of anomaly management for autonomous systems is rarely discussed, highlighting the need for a thorough understanding of the mechanisms necessary for the seamless operation of autonomous systems in the context of intralogistics. In this work, we (i) define a comprehensive framework for anomaly management in intralogistics that incorporates proactive strategies aimed at anticipating and preventing anomalies, enabling early detection and isolation, supporting adaptive response and recovery, and fostering continuous learning; (ii) present the definitions of anomaly and emergency, as well as anomaly handling and anomaly management; (iii) provide a comparison of our framework with existing solutions in the literature, highlighting their advantages and limitations; and (iv) identify essential future research directions, thereby outlining relevant topics for exploration and study. These contributions aim to pave the way for more resilient and adaptive autonomous systems in intralogistics.