Resumen
In the ever-evolving landscape of warehousing, the integration of unmanned ground vehicles (UGVs) has profoundly revolutionized operational efficiency. Despite this advancement, a key determinant of UGV productivity remains its energy management and battery placement strategies. While many studies explored optimizing the pathways within warehouses and determining ideal power station locales, there remains a gap in addressing the dynamic needs of energy-efficient UGVs operating in tandem. The current literature largely focuses on static designs, often overlooking the challenges of multi-UGV scenarios. This paper introduces a novel algorithm based on affinity propagation (AP) for smart battery and charging station placement in modern warehouses. The idea of the proposed algorithm is to divide the initial area into multiple sub-areas based on their traffic, and then identify the optimal battery location within each sub-area. A salient feature of this algorithm is its adeptness at determining the most strategic battery station placements, emphasizing uninterrupted operations and minimized downtimes. Through extensive evaluations in a synthesized realistic setting, our results underscore the algorithm?s proficiency in devising enhanced solutions within feasible time constraints, paving the way for more energy-efficient and cohesive UGV-driven warehouse systems.