基于时空网络的自动化集装箱码头自动化导引车路径规划

针对自动化集装箱码头水平搬运作业中自动化导引车路径冲突问题，提出一种基于时空网络的路径优化方法。对于单个运输需求，首先，将路网离散化为网格网络，设计依据时间可更新的时空网络；其次，以任务完工时间最短为目标，基于时空网络下可用路段集合来建立车辆路径优化模型；最后，在时空网络上运用最短路径算法求解得最短路径。对于多个运输需求，为避免路径冲突，根据当前运输需求的路径规划结果更新下一个运输需求的时空网络，并通过迭代最终获得满足规避碰撞和缓解拥堵条件的路径规划。计算实验中，与基本最短路径求解策略（求解算法P）相比，所提方法的碰撞次数降低为0并且最小相对距离始终大于安全距离；与停车等待求解策略（求解算法SP）相比，所提方法最多减少任务总延误时间24　s，且明显降低延误任务占比以及路网平均拥堵度，最大降低程度分别为2.25%和0.68%。实验结果表明，所提方法能够有效求解大规模冲突规避的路径规划问题，并显著提高自动化导引车的作业效率。

Path planning for automated guided vehicles based on tempo-spatial network at automated container terminal

In order to solve the path conflict problem of automated guided vehicles in horizontal handling operations of automated container terminals, a path optimization method based on tempo-spatial network was proposed. For single transportation demand, firstly, the road network was discretized into grid network, and a tempo-spatial network which is updateable by time was designed. Secondly, the minimum of the completion time was taken as an objective, and a vehicle path optimization model was established based on the set of available road segments under tempo-spatial network. Finally, the shortest path algorithm was used on the tempo-spatial network to obtain the shortest path. For multiple transportation demands, in order to avoid conflicts between paths, the tempo-spatial network of the next transportation demand was updated according to the path planning results of the current transportation demand, and the path planning meeting the collision avoidance and congestion easing conditions were finally obtained through iteration. In the calculation experiment, compared with the basic shortest path solution strategy (solving algorithm P), the proposed method has the number of collisions reduced to 0 and the minimum relative distance always greater than the safety distance; compared with the parking waiting solution strategy (solving algorithm SP), the proposed method has total delay time of task reduced to 24 s, the proportion of delayed tasks and average congestion rate of road network significantly reduced, and the maximum reduction were 2.25% and 0.68% respectively. The experimental results show that the proposed method can effectively solve large-scale conflict-free path planning problems and significantly improve the operation efficiency of automated guided vehicles.