ArticleSensors (Basel, Switzerland)2026
A Digital Twin-Driven Sensing and Fuzzy Decision Framework for Safety Monitoring of Autonomous Mobile Robot Systems in Intralogistics.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The increasing use of autonomous mobile robots (AMRs) in internal logistics systems improves operational efficiency. However, it also introduces challenges related to safety, reliability, sensor-based monitoring and human-robot interaction. This study proposes a sensor-driven Digital Twin and fuzzy decision-support framework for operational risk monitoring in AMR-based transportation systems. The proposed approach integrates Digital Twin technology with fuzzy logic methods to support continuous sensing, operational data acquisition, and data-driven risk evaluation in autonomous logistics environments. In the proposed framework, the Digital Twin acts as a continuous monitoring and early-warning environment. It enables continuous observation of system states, robot condition, navigation performance, traffic intensity and operational disturbances. To support decision-making under uncertainty, the fuzzy Analytic Hierarchy Process (fuzzy AHP) is applied to determine the relative importance of selected safety and reliability indicators. These indicators include condition monitoring parameters, mean time between failures, sensor-related disturbances and task completion performance. Subsequently, a hierarchical Mamdani fuzzy inference system is used to evaluate the operational risk level of the transportation system based on aggregated KPI values derived from Digital Twin data. The applicability of the proposed approach is illustrated through a case study involving multiple AMRs operating in a dynamic intralogistics environment. The results indicate that the integration of sensor-based Digital Twin monitoring with fuzzy decision-support mechanisms improves operational risk visibility and supports more effective risk identification and management in Industry 4.0 intralogistics systems.
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