An optimized path planning approach for automatic parking using hybrid A* bidirectional search

IF 3.5 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Intelligence Pub Date : 2024-12-21 DOI:10.1007/s10489-024-05915-y
Wenrui Jin, Jiaxue Li, Xiaoxiao Lv, Tao Zhang
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Abstract

Path planning in automatic parking is a significant challenge due to constrained parking spaces and numerous obstacles. To enhance both the safety and efficiency of the planned path, this paper proposes a bidirectional hybrid A* algorithm for narrow spaces with a high density of obstacles. A vehicle obstacle avoidance method that incorporates rectangular expansion through numerical analysis is proposed to achieve collision-free navigation. Meanwhile, a safety cost is integrated into the hybrid A* search algorithm to maintain a sufficient safety distance between the planned path and obstacles. Additionally, to enhance the efficiency of path planning, a bidirectional search method is combined with the hybrid A* algorithm, with the addition of a bidirectional cohesive item cost. Finally, simulation experiments are conducted to generate parking paths for both vertical and parallel parking scenarios. The simulation results indicate that the proposed algorithm obtains a sufficient safety distance, reduced search time, and fewer expanded nodes. Meanwhile, the stability and adaptability of the proposed method are analyzed. The comparison with other algorithms suggests that the proposed algorithm provides a larger safety distance and shorter search time.

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基于混合A*双向搜索的自动泊车路径优化规划方法
由于停车空间有限且障碍物众多,自动泊车中的路径规划是一项重大挑战。为了提高规划路径的安全性和效率,本文针对障碍物密集的狭窄空间提出了一种双向混合 A* 算法。通过数值分析,提出了一种结合矩形扩展的车辆避障方法,以实现无碰撞导航。同时,在混合 A* 搜索算法中加入了安全成本,使规划路径与障碍物之间保持足够的安全距离。此外,为了提高路径规划的效率,还将双向搜索方法与混合 A* 算法相结合,并增加了双向凝聚项成本。最后,通过仿真实验生成了垂直和平行停车场景下的停车路径。仿真结果表明,所提出的算法获得了足够的安全距离,缩短了搜索时间,减少了扩展节点。同时,分析了所提方法的稳定性和适应性。与其他算法的比较表明,所提算法的安全距离更大,搜索时间更短。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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