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The Unmanned Ground Vehicle's local path plan algorithm that adapted weight according to obstacles in local area

机译:根据当地障碍物调整重量的无人地面车辆的局部路径计划算法

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The existing path plan algorithms repeat process until reach to goal position (from the start position). As result of process, path plan algorithms create the optimal paths. From the algorithm point of view, created paths are optimal paths because paths are the shortest and lowest. But this path does not consider unit''s moving characteristics. The current path plan algorithms have been developing that solving and supplementing the part of problems about the grid map environment. According to the passage, methods of the solving and supplementing can divide two ways. First method is path smoothing, second method is the Field D∗ algorithm. If the path smoothing is adapted in Global size (Global path in Global map), in such that case, the time constraint is not important but the optimal path will be modified. And modification of the optimal path like to has serious problems. Field D∗ algorithm is powerful path plan algorithm and this algorithm is not postprocessing like a path smoothing. And in a way it guarantees real-time path creations. But local size (local path in local map)''s real-time do not guarantee perfectly. On the basis of facts, we suggest the Unmanned Ground Vehicle (we abbreviate the word by ''UGV'')''s new path plan algorithm that included UGV''s moving characteristics.
机译:现有的路径计划算法重复此过程,直到到达目标位置(从起始位置开始)。作为过程的结果,路径计划算法会创建最佳路径。从算法的角度来看,创建的路径是最佳路径,因为路径是最短和最低的。但是此路径未考虑单元的移动特性。当前的路径计划算法正在开发中,用于解决和补充有关网格地图环境的部分问题。根据段落,解决方法和补充方法可以分为两种方式。第一种方法是路径平滑,第二种方法是Field D 算法。如果在“全局大小”(“全局地图”中的“全局路径”)中调整了路径平滑,则在这种情况下,时间限制并不重要,但是会修改最佳路径。而对最佳路径的修改就存在严重的问题。字段D 算法是功能强大的路径计划算法,并且该算法不像路径平滑那样进行后处理。并且以某种方式保证了实时路径的创建。但是本地大小(本地地图中的本地路径)的实时性并不能完美保证。基于事实,我们建议无人地面车辆(我们将其缩写为“ UGV”)的新路径规划算法,其中包括UGV的移动特性。

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