Parking in large metropolitan areas is often a time-consuming task with further implications toward traffic patterns that affect urban landscaping. Reducing the premium space needed for parking has led to the development of automated mechanical parking systems. Compared to regular garages having one or two rows of vehicles in each island, automated garages can have multiple rows of vehicles stacked together to support higher parking demands. Although this multi-row layout reduces parking space, it makes the parking and retrieval more complicated. In this work, we propose an automated garage design that supports near 100% parking density. Modeling the problem of parking and retrieving multiple vehicles as a special class of multi-robot path planning problem, we propose associated algorithms for handling all common operations of the automated garage, including (1) optimal algorithm and near-optimal methods that find feasible and efficient solutions for simultaneous parking/retrieval and (2) a novel shuffling mechanism to rearrange vehicles to facilitate scheduled retrieval at rush hours. We conduct thorough simulation studies showing the proposed methods are promising for large and high-density real-world parking applications.

翻译：在大型都市区域，停车往往是一项耗时的工作，并对影响城市景观的交通模式产生进一步影响。减少停车所需的黄金空间促使了自动化机械停车系统的发展。与每个区域内仅有一两排车辆的普通车库相比，自动化车库可堆叠多排车辆以满足更高的停车需求。尽管这种多排布局减少了停车空间，但同时也使泊车与取车操作更为复杂。本研究提出一种支持近100%停车密度的自动化车库设计方案。将多辆车同时泊入与取出的问题建模为一类特殊的多机器人路径规划问题，我们提出了相关算法以处理自动化车库的所有常规操作，包括：(1) 可同时实现泊车/取车可行且高效解的最优算法与近优方法；(2) 一种新颖的车辆重排机制，用于在高峰时段为预定取车任务调整车辆位置。我们进行了全面的仿真研究，结果表明所提方法在大规模高密度实际停车应用中具有良好前景。