论文标题
在2D中未知环境中自动机器人找到大约最短的途径的多种射击方法
Multiple Shooting Approach for Finding Approximately Shortest Paths for Autonomous Robots in Unknown Environments in 2D
论文作者
论文摘要
视力范围有限的自主机器人在避免多边形障碍的2D环境中找到了目标的途径。在发现环境图的过程中，机器人必须返回到先前标记的某些位置，机器人遍历要返回的区域被定义为线段束的序列。本文提出了一种新型算法，用于根据多次拍摄的方法找到沿线段束序列的大约最短路径。提出了该方法的三个因素，包括捆绑分区，共线条件和射击点的更新。然后，我们表明，如果共线条件保持，则确定问题的最短路径，否则，通过更新方法收敛到最短路径所获得的路径序列。该算法在Python中实现，一些数值示例表明，使用我们的方法使用自主机器人的路径计划的运行时间比使用Li和Klette在Euclidean最短路径中使用Li和Klette的橡皮筋技术的运行时间更快，Springer，53-89（2011）。
An autonomous robot with a limited vision range finds a path to the goal in an unknown environment in 2D avoiding polygonal obstacles. In the process of discovering the environmental map, the robot has to return to some positions marked previously, the regions where the robot traverses to return are defined as sequences of bundles of line segments. This paper presents a novel algorithm for finding approximately shortest paths along the sequences of bundles of line segments based on the method of multiple shooting. Three factors of the approach including bundle partition, collinear condition, and update of shooting points are presented. We then show that if the collinear condition holds, the exactly shortest paths of the problems are determined, otherwise, the sequence of paths obtained by the update of the method converges to the shortest path. The algorithm is implemented in Python and some numerical examples show that the running time of path-planning for autonomous robots using our method is faster than that using the rubber band technique of Li and Klette in Euclidean Shortest Paths, Springer, 53-89 (2011).
