Enhanced Spatiotemporal Coordination for Multi-Robot Systems in Smart Warehousing: A Digital Twin-Enabled Approach

Résumé fourni par la source

This study introduces a spatiotemporal coordination framework enhanced through digital twin synchronization for robotic warehouse systems, with three conceptual advances overcoming conventional optimization bottlenecks. First, a 3D node expansion architecture using hierarchical temporal windows enables simultaneous path-temporal optimization through dynamic collision matrices, reducing spatial conflicts by 78–92% compared to single-space planning approaches. Second, our hybrid search algorithm integrates adaptive congestion models with prioritized replanning, achieving 95.3% path efficiency while the auction-based path planning reduces total travel distances by 18–25% compared to random allocation algorithms, through synergistic optimization of movement coordination and temporal sequencing. Finally, the virtual-real synchronization platform establishes a closed-loop verification system demonstrating 93% deadlock reduction in high-density operations through predictive traffic simulations, resolving a critical scalability limitation in existing multi-robot architectures. Validation across 6–20 robot scenarios confirms real-time responsiveness with 2.3s average replanning latency, while enhanced auction mechanisms improve task allocation fairness by 38–65% compared to traditional bidding systems. These innovations collectively advance both theoretical models of spatiotemporal constraints and practical implementations of digital twin verification for industrial automation.

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