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HOW AUTONOMOUS ROBOTS AND INDOOR MAPPING IMPROVE WAREHOUSE SAFETY
Abstract
This study explores the implementation of AI-driven Autonomous Mobile Robots (AMRs) in a mid-sized manufacturing warehouse in the Czech Republic, replacing traditional forklift operations to enhance occupational health and safety (OHS) and optimize internal logistics efficiency. The research integrates advanced indoor geospatial technologies - including LiDAR mapping, real-time positioning (Ultra-Wideband), and spatial data analytics - into warehouse workflows. Although full AMR deployment has not yet been realized, a preparatory phase was conducted involving comprehensive risk assessment and spatial simulations using detailed facility layout and historical incident reports from 2022 to 2025. The developed digital twin models successfully identified collision hotspots, high-risk ergonomic zones, and areas of traffic congestion. The simulations demonstrated significant reductions in accident likelihood, improved route optimization, lower congestion levels, and reduced physical strain on warehouse personnel. AI algorithms applied in this study include SLAM-based navigation, real-time obstacle detection, adaptive path recalculation, and predictive traffic management supported by machine learning. In addition, a spatial monitoring and real-time risk evaluation framework was developed, offering scalable application to various industrial environments. Comparative case studies from Germany, the United States, and Japan confirm that similar AMR integrations lead to measurable safety improvements. These results highlight the effective synergy between geoinformatics, artificial intelligence, and occupational safety within Industry 4.0.
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