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The Exact Euclidean Distance Transform: A New Algorithm for Universal Path Planning

机译:精确的欧氏距离变换:一种通用路径规划的新算法

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The Path-Planning problem is a basic issue in mobile robotics, in order to allow the robots to solve more complex tasks, for example, an exploration assignment in which the distance given by the planner is taken as a utility measure. Among the different proposed approaches, algorithms based on an exact cell decomposition of the environment are very popular. In this paper, we present a new algorithm for universal path planning in cell decomposition, using a raster scan method for computing the Exact Euclidean Distance Transform (EEDT) for each cell in the map. Our algorithm computes, for every cell in the map, the point sequence to the goal. For each sequence, the sub-goals are selected near to the vertices of the obstacles, reducing the total distance to the goal without post processing. At the end, we obtain a smooth path up to the goal without the need for post-processing. The paths are computed by visibility verification among the cells, exploiting the processing performed in the neighbouring cells.
机译:路径规划问题是移动机器人技术中的一个基本问题,为了使机器人能够解决更复杂的任务,例如,探索任务,其中以计划者给出的距离作为效用度量。在提出的不同方法中,基于环境的精确细胞分解的算法非常流行。在本文中,我们提出了一种用于细胞分解中通用路径规划的新算法,该算法使用栅格扫描方法来计算地图中每个细胞的精确欧几里德距离变换(EEDT)。我们的算法为地图中的每个像元计算目标的点序列。对于每个序列,将在障碍物的顶点附近选择子目标,从而无需进行后期处理即可减少到目标的总距离。最后,我们无需进行后期处理即可获得通向目标的通畅路径。通过利用相邻小区中执行的处理,通过小区之间的可见性验证来计算路径。

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