高密集度AGV快递包裹分拣系统的路径规划

基于自动引导小车（AGV）的快递包裹自动分拣系统是智能物流的研究热点，路径规划是其关键问题之一.在快递包裹分拣系统中，AGV具有高密集性和车辆数量较大的特点，这种情况极易造成AGV拥堵，使得整个系统的性能降低.针对此问题本文提出可避免拥挤的CAA^*（Congestion-avoidable A^*）算法，该算法以A^*算法为基础，引入动态属性节点，建立动态环境模型，对各个节点可能发生的拥挤情况进行预测，判断是否存在潜在的拥挤节点，在路径规划过程中绕过潜在的拥堵节点，避免发生拥堵现象.实验结果表明，本文所提的CAA^*路径规划方法在具有高密集度和较大规模的AGV场景中，能有效避免拥堵，从而提高场地AGV的密集度和系统的分拣效率.对实际应用场地的仿真表明，本文的算法比传统的A^*算法AGV密集度提高了28.57%，系统分拣效率提高了24.29%.

Path Planning of High Density AGV Parcel Sorting System

Automatic parcel sorting system using Automatic Guided Vehicle (AGV) is a research hotspot of intelligent logistics, in which path planning is one of the key issues. In an express sorting system, AGV has the characteristics of high density and large number of vehicles. This situation will cause congestion, making the performance of the whole system decrease. Aiming at this problem, this study proposes a CAA^* (Congestion-Avoidable A^*) algorithm that is able to avoid congestion. Based on A^* algorithm, the proposed algorithm introduces dynamic attribute nodes and establishes dynamic environment model to predict the possible crowding situation of each node, judge whether there are potential congestion nodes in the path planning process and bypass possible congestion nodes. Experimental results show that the proposed CAA^* path planning method can effectively reduce congestion in high-density and large-scale AGV scenarios, thereby improving the density of AGV and system sorting efficiency. Simulation results on practical application sites show that the proposed algorithm improves the AGV density by 28.57% and the sorting efficiency by 24.29% compared with the traditional A^* algorithm.