面向大型激光装置集成安装的机器人自动路径规划

Automatic path planning of robot for integrated installation of large laser device

  • 摘要: 针对大型激光装置集成安装过程中的机器人路径规划问题,提出一种简单有效的改进A*算法。该算法较传统A*算法进行了三步改进:第一步是限制可行走方向,避免出现传统A*算法发生穿越障碍物情况;二是将其启发函数优化为加权曼哈顿距离函数,加速向x方向或者y方向扩展节点,改善限制可行走方向带来的遍历节点数激增现象;三是引入转弯惩罚项,减少路径规划过程中的转弯次数,提高路径规划搜索效率和质量。在不同大小的栅格地图中验证三步改进A*算法的性能,并与传统A*算法进行比较。实验结果表明,简单地图中,三步改进A*算法遍历节点数略高于传统A*算法,转弯次数与传统A*算法相当,但路径避障性能明显优于传统A*算法,更有利于机器人安全行走。复杂地图中,综合考虑遍历节点数、转弯次数和路径长度的优先关系后,可以实现调节三步改进A*算法参数至路径规划结果最优。

     

    Abstract: A simple and effective improved A* algorithm is proposed to solve the problem of robot path planning in the integrated installation of large-scale laser devices. Compared with the traditional A* algorithm, the algorithm has been improved in three steps. Firstly, the walking direction is limited, which avoids the phenomenon of crossing obstacles occurred in the traditional A* algorithm; Secondly, the heuristic function is optimized as a weighted Manhattan distance function, which accelerates the expansion of nodes in the x direction or y direction, and reduces the surge of traversal nodes caused by limiting the walking direction. Thirdly, the turning penalty term is introduced to reduce the number of turns in the path planning process, and improve the search efficiency and quality. The performance of the three-step improved A* algorithm is verified in different size raster maps, and compared with the traditional A* algorithm. Experimental results show that in simple maps, the number of nodes traversed by the three-step improved A* algorithm is slightly higher than that of the traditional A* algorithm, and the number of turns is equivalent to that of the traditional A* algorithm, but the obstacle avoidance performance is obviously better than that of the traditional A* algorithm, which is more conducive to the safe walking of robots. In complex maps, considering the priority relationship of traversal nodes, turn times and path length, the parameters of the three-step improved A* algorithm can be adjusted to obtain the optimal path planning result.

     

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