A dynamic path planning approach for dense, large, grid-based automated guided vehicle systems

Abstract Real-time path planning for large, dense grid-based automated guided vehicle (AGV) systems, used for example to sort parcels, is challenging. Most approaches described in the literature are not fast enough for real-time control or are not able to avoid congestion. This paper presents a dynamic approach using a graph-representation of the grid system layout with vertex weights that are updated over time. By means of an extensive discrete-event simulation, we show that the proposed path planning approach significantly increases the throughput compared to existing approaches. Furthermore, it enables the recovery from deadlock situations.