An Emergency Dynamic Isolation System for Human-Robot Collaborative Warehousing: A Performance Study

With the rising living standards and shifting consumer habits, an increasing number of individuals have embraced online shopping. Concurrently, merchants are deploying intensive promotional campaigns, particularly during annual events such as "618," "Double 11," and live-streaming sales, which often trigger instantaneous "order explosion" surges. To address the efficiency-safety conflict arising from intense spatial competition within large-scale, high-density human-robot collaborative smart warehouses under such emergency conditions, this paper proposes an Emergency Dynamic Isolation System (EDIS). The system utilizes Ultra Wide Band (UWB) positioning and Long Short-Term Memory (LSTM) network-based trajectory prediction to perceive real-time human and robot dynamics. It then calculates and generates temporary, minimized virtual safety boundaries based on an improved Dynamic Risk Field Model (DRFM). To evaluate its effectiveness, a high-fidelity discrete-event simulation model was developed in Python, simulating the operation of a super-large book warehouse—with a total area of 30,000 square meters, storing 100,000 bins and 20,000 pallets, and equipped with 285 bin-carrying robots, 57 shuttle robots, 20 pallet-pulling robots, 15 autonomous three-directional forklifts, and 70 operators—under a 500% surge in order volume. Comparative experiments with traditional static physical isolation and static electronic fencing schemes demonstrate that, while ensuring zero collision risk, EDIS improves the overall order completion rate by 46.8%, reduces the average walking distance of pickers by 31.2%, achieves a peak dynamic space-sharing rate of 78.4% in key aisles, and decreases production interruptions due to avoidance maneuvers by 94.7% during the 6-hour emergency peak period. This study provides a quantitatively validated innovative solution for ultra-large, high-density human-robot collaborative smart warehouse systems to cope with extreme operational fluctuations.