Database-Based Cooperative Scheduling Optimization of Multiple Robots for Smart Warehousing

Abstract

This study investigates the current state and future directions of cooperative scheduling optimization for multiple robots in smart warehousing environments. With the rapid growth of logistics automation, optimizing the collaboration between intelligent robots has become essential for improving warehouse efficiency and adaptability. The research employs a bibliometric analysis based on the Web of Science (WoS) database, using VOSviewer for keyword co-occurrence, clustering, and density visualization to identify key research hotspots, knowledge structures, and technological trends. The analysis categorizes the field into four major research clusters: robot path planning and navigation, warehouse system optimization and order picking, algorithm design and performance evaluation, and the application of emerging technologies such as edge computing and cloud robotics. Results shows a growing emphasis on dynamic scheduling, real-time data integration, and multi-objective optimization, with increasing use of technologies like deep reinforcement learning and digital twins. The study also incorporates real-world case comparisons from leading domestic and international enterprises, revealing implementation challenges and performance benchmarks. Although promising advancements are evident, issues such as fragmented data systems, limited real-time responsiveness, and insufficient cross-disciplinary integration persist. The study concludes that future research should focus on improving environmental adaptability through edge computing, standardizing robot collaboration protocols, and enhancing system robustness via real-time database architectures. By bridging theoretical insights with practical needs, this research offers a comprehensive foundation for developing next-generation intelligent warehousing systems based on coordinated multi-robot scheduling.