Continuity-Aware Multi-Stage Task Preloading for Multi-AGV Systems Under Temporary Central Communication Interruptions

To address redundant motion caused when centrally scheduled multi-AGV systems continue executing stale commands during temporary communication interruptions, this paper proposes Continuity-Aware Multi-stage Preloading (CAMP). In the offline phase, SearchTeacher generates multi-stage supervision of relative vehicle priorities through look-ahead candidate evaluation, which is used to train a Context-Aware Gated Recurrent Unit (CA-GRU). When central communication is available, the frozen CA-GRU predicts stage-wise vehicle priorities and continuous task parameters; a constraint-aware decoder combines stage reachability, energy states, and role budgets to generate multi-stage task packets with explicit validity intervals and write them to local caches. During a communication interruption, vehicles execute the cached tasks locally stage by stage. System-level experiments with 30 independent random seeds and five interruption durations show no statistically significant difference between CAMP and SingleStep in longest continuous service, while total travel distance, relative energy consumption, and the mean number of moving AGVs are reduced by 69.5%, 71.1%, and 73.6%, respectively. The final frozen CA-GRU achieves 98.71% pairwise ranking accuracy on an independent test set, and CAMP achieves an approximately 31.8× speedup in task-packet generation relative to SearchTeacher. These results show that CAMP can maintain service continuity comparable to SingleStep while substantially reducing fleet motion and online computational cost under the tested communication-interruption conditions.