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Based on automated guided vehicle (AGV), the intelligent parking system provides a novel solution to the difficulty of parking in large cities. The automation of parking/pick-up in the system hinges on the path planning efficiency of the AGV. Considering the numerous disconnected paths in intelligent parking systems, this paper introduces the fallback strategy to improve ant colony optimization (ACO) for path planning in AGV-based intelligent parking system. Meanwhile, the valuation function was adopted to optimize the calculation process of the heuristic information, and the reward/penalty mechanism was employed to the pheromone update strategy. In this way, the improved ACO could plan the optimal path for the AGV from the starting point to the destination, without sacrificing the search efficiency. Next, the optimal combination of ACO parameters was identified through repeated simulations. Finally, a typical parking lot was abstracted into a topological map, and used to compare the path planning results between the improved ACO and the classic ACO. The comparison confirms the effectiveness of the improved ACO in path planning for AGV-based intelligent parking system.
Wang et al. (Wed,) studied this question.