姜媛媛,张阳阳.改进 8 邻域节点搜索策略 A ∗ 算法的路径规划[J].电子测量与仪器学报,2022,36(5):234-241
改进 8 邻域节点搜索策略 A ∗ 算法的路径规划
Improved path planning of A∗algorithm of domain node search strategy 8
  
DOI:
中文关键词:  A ∗ 算法  冗余节点  节点搜索  垂距限值法  8 邻域
英文关键词:A ∗ algorithm  redundant nodes  search node  perpendicular distance methods  eight areas
基金项目:安徽省重点研究与开发计划(202104g01020012)、安徽理工大学环境友好材料与职业健康研究院研发专项基金(ALW2020YF18)项目资助
作者单位
姜媛媛 1. 安徽理工大学电气与信息工程学院,2. 安徽理工大学环境友好材料与职业健康研究院(芜湖)芜湖 
张阳阳 1. 安徽理工大学电气与信息工程学院 
AuthorInstitution
Jiang Yuanyuan 1. School of Electrical and Information Engineering, Anhui University of Science and Technology,2. Institute of Environment-Friendly Materials and Occupational Health, Anhui University of Science and Technology 
Zhang Yangyang 1. School of Electrical and Information Engineering, Anhui University of Science and Technology 
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中文摘要:
      针对传统 A ∗ 算法所规划路径存在易触碰障碍物,冗余节点过多等问题。 提出了一种改进 8 邻域节点搜素策略 A ∗ 算法 的路径规划。 首先,在 A ∗ 算法中改进节点搜索条件,使得获得的节点与周围障碍物节点保持一定安全距离;然后利用垂距限 值法剔除改进后路径上的冗余节点,并保留关键节点;最后,基于关键节点利用 B 样条曲线拟合出一条平滑路径。 在不同规模 的多障碍物地图环境下进行多次实验与对比,结果表明,相比于传统 A ∗ 算法,提出的改进 A ∗ 算法,获得的路径节点与障碍物 节点的安全距离平均保持在 0. 46,路径节点平均减少了 66. 8%,有效提升机器人的工作效率和安全性。
英文摘要:
      Traditional A ∗ algorithms plan paths that run the risk of hitting obstacles and create too many redundant nodes. For these problems, a path planning with improved search 8-neighborhood node strategy A ∗ algorithm is proposed. Firstly, the node search condition is improved in the A ∗ algorithm so that the obtained nodes maintain a safe distance from the surrounding obstacle nodes. Secondly, the redundant nodes on the improved path are removed using the vertical distance limit method, and the critical nodes are retained. Finally, a smooth path is obtained by fitting a B spline curve to the key nodes to achieve a smooth path. By conducting several experiments and comparisons in multiple obstacle map environments of different scales. The results show that the proposed improved A ∗ algorithm, compared to the traditional A ∗ algorithm, maintains an average of 0. 46 path nodes to obstacle nodes and reduces path nodes by an average of 66. 8%, effectively improving the efficiency and safety of the robot.
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