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This paper proposes an improved safe adaptive ant colony algorithm for underground parking lot path planning problems. Firstly, by enhancing the heuristic function and introducing the distance to the target node and the adaptive enhancement coefficient, ants are inclined to move towards the target node to avoid falling into local optima. Secondly, the adoption of Gaussian distribution for initializing pheromones and a limited pheromone update strategy enhances the search efficiency of the algorithm. Finally, a comprehensive scoring strategy is designed to select the optimal path. Simulation results from three different scale environment maps demonstrate that this algorithm outperforms traditional A star algorithm in target orientation and surpasses traditional and improved ant colony algorithms in terms of turning times and time. With efficiency and practicality, it can be effectively applied to the problem of underground parking lot path planning, providing an effective solution to improve urban traffic efficiency and alleviate parking difficulties
Wang et al. (Sun,) studied this question.