Toward Efficient Physical and Algorithmic Design of Automated Garages

TL;DR

This paper introduces an automated garage design supporting near 100% parking density by modeling parking and retrieval as a multi-robot path planning problem, with algorithms for efficient operations and vehicle shuffling.

Contribution

It presents a novel multi-robot path planning approach and algorithms for high-density automated parking systems, including a vehicle shuffling mechanism for scheduled retrieval.

Findings

Algorithms achieve efficient parking and retrieval operations.

Simulation results show high-density parking feasibility.

Shuffling mechanism improves retrieval scheduling.

Abstract

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.