In extant literature the terms automated guided vehicle (AGV), autonomous mobile robot (AMR), and mobile robot (MR) are used with different interpretations. Since the terms are closely related to technical characteristics, features, and possible applications, inconsistent use of these terms hinders their interoperability, and complicates the integration of systems across the warehousing domain. This paper defines the terms AGV, AMR, MR, based on the definitions from a systematic literature review. The study relies on English and German-language research from 2000 until 2025. Risk of bias in inclusion of studies was assessed using predefined evidence grades and content relevance grades. In total, 177 publications were included in the final analysis, clustered by locus of control, freedom of manipulation, collaborative ability, and software capability. The key findings show that the definitions of AGVs overlap with new AMR and MR concepts. The decisive factor is the ability of the vehicle or system to be autonomous. Building on the new definitions, we introduce a spectrum of autonomy maturity levels, ranging from remotely-controlled (zero autonomy) to fully autonomous (independent) systems. Understanding the nature and appropriate autonomy level of a system is essential to support scalability, reduce information asymmetries among vendors and users, and accelerate innovation in the rapidly evolving field of mobile robotics. We also identify implications for future research based on our findings in the concluding section.
Sven Franke, René B. M. de Koster, Christopher Reining et al.· Journal of Intelligent and R...· 1 citation
Anomaly detection is a safety-critical machine learning problem with applications ranging from fraud detection to network intrusion prevention and industrial monitoring. Despite the large number of proposed anomaly detection algorithms, many novel methods claim state-of-the-art performance. However, many authors do so under benchmark settings that are not aligned with one another. This lack of comparability raises concerns regarding the reproducibility and reliability of anomaly detection benchmarks. In this work, we study the impact of common benchmarking choices on the stability of algorithm rankings. Using seven representative anomaly detection algorithms and 690 datasets from the OddBench benchmark suite, we analyze how rankings change under varying dataset selections, evaluation metrics, hyperparameter configurations, and random seeds. To quantify this effect, we introduce a rank instability metric measuring the variability of algorithm rankings across benchmark settings. Our results show that algorithm rankings in anomaly detection are highly unstable. In many cases, almost every competitive algorithm can appear as the best-performing method under some benchmark configuration. Among the studied factors, dataset selection and hyperparameter choice contribute most strongly to ranking uncertainty, while random seeds and evaluation metrics have a comparatively limited impact. We also observe that reliable benchmarking requires substantially larger and more diverse dataset collections than the ones commonly used in prior work.
Simon Klüttermann, Jérôme Rutinowski, Frederik Polachowski et al.· 0 citations
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