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吴飞龙,郭世永. 融合改进A*和动态窗口法的AGV动态路径规划[J]. 科学技术与工程, 2020, 20(30): 12452-12459.
Wu Feilong,郭世永.AGV’s Dynamic Path Planning Based on Fusion of improved A * and DWA Algorithms[J].Science Technology and Engineering,2020,20(30):12452-12459.
融合改进A*和动态窗口法的AGV动态路径规划
AGV’s Dynamic Path Planning Based on Fusion of improved A * and DWA Algorithms
投稿时间:2019-12-28  修订日期:2020-08-03
DOI:
中文关键词:  关键词:AGV  A*算法  DWA算法  安全域值  实时动态避障
英文关键词:[Keywords] AGV  A * algorithm    DWA algorithm  security domain value  real-time dynamic obstacle avoidance
基金项目:面上项目(51678320)
     
作者单位
吴飞龙 青岛理工大学机械与汽车工程学院
郭世永 青岛理工大学机械与汽车工程学院
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中文摘要:
      摘要:路径规划对AGV完成自主导航起着关键性的作用,针对这点出发,本文提出一种全局采用改进的A*算法,局部在保证全局最优的情况下采用融合A*和DWA两种算法,首先加入AGV的位置信息,在传统的评价函数基础上设置了包含代价函数和启发函数的权重函数,其次,将传统的8个搜索方向,变成了5个,提高了路径搜索效率,为了进一步增加路径的平顺性,对节点进行优化,删除了多余的转折点,保留下关键的转折点,然后在此基础上又设置了安全域值,进一步删除多余的转折点,从而保证了全局路径最优,为了实现AGV实时动态避障,本文将A*和DWA两种算法融合,进行在线实时规划路径,设计了一种基于全局最优路径的圆滑路径曲线,经过仿真,本文提出的算法在路径长度,机器人平均转折角度,运行时间等都大大减少,最后,通过实验进一步验证了算法的可行性。
英文摘要:
      [Abstract] Path planning plays a key role in AGV's completion of autonomous navigation. In view of this, this paper proposes a globally adopted improved A* algorithm, and partially uses the fusion of A* and DWA while ensuring global optimality. Firstly, the algorithm adds the position information of the AGV, and sets a weight function including a cost function and a heuristic function on the basis of the traditional evaluation function. Secondly, it turns the traditional 8 search directions into 5 to improve the efficiency of path search. In order to further increase the smoothness of the path, the nodes are optimized, the redundant turning points are deleted, the key turning points are retained, and then the security domain value is set on this basis, and the redundant turning points are further deleted, thereby ensuring the optimal global path. In order to achieve AGV real-time dynamic obstacle avoidance, this paper combines two algorithms, A * and DWA, to plan the path online in real time, and designs a smooth path curve based on the global optimal path. After simulation, the algorithm proposed in this paper , The robot's average turning angle, running time, etc. have been greatly reduced. Finally, experiments further verify the feasibility of the algorithm.
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