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Multi-robot navigation in a maze.

机译:迷宫中的多机器人导航。

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摘要

We consider path planning for multiple mobile robots navigating in a rectangular maze divided into cells of equal size. Some cells contain permanent obstacles and some are empty and available for a robot to move into. All robots are synchronized and each robot is allowed to move into an empty cell adjacent to the one that it presently occupies. The objective is to determine a set of collision-free paths that lead each robot from a specified initial location to a specified goal location in minimum time.;Our study provides new near-optimal on-line algorithms for safe navigation of limited-vision robots. We also provide exact expressions for the mean path lengths within mazes of arbitrary architectures.;Two general approaches are investigated: centralized planning and distributed planning. Using centralized-planning, a single processor with full knowledge of the maze topology and of the initial and goal locations of the robots determines the time-optimal paths using standard graph search techniques. The central planners provide optimal solutions (whenever one exists) but they are computationally prohibitive--the complexity of the graph search grows exponentially in the number of navigating robots. In the distributed-planning version, minimum-length paths are first computed for each robot independently (i.e., under the assumption that each robot navigates the maze alone). The individually-optimal paths are then modified locally to produce collision-free paths. Our modifications are based on limiting the robots' visible neighborhood--robots adjust their independently-planned paths when they "see" other robots in their vicinity and realize the threat of collision. In the limit, robots are totally "blind" and they learn about the existence of other robots by colliding with them.
机译:我们考虑在矩形迷宫中导航的多个移动机器人的路径规划,这些迷宫被分成大小相等的单元。有些单元格包含永久性障碍物,有些则是空的,可用于机器人进入。所有机器人均已同步,并且允许每个机器人移入与其当前占用的机器人相邻的空单元。目的是确定一组无冲突的路径,这些路径可在最短的时间内将每个机器人从指定的初始位置引导到指定的目标位置。;我们的研究提供了新的近最佳在线算法,用于有限视野机器人的安全导航。我们还提供了任意架构迷宫中平均路径长度的精确表达式。研究了两种通用方法:集中式规划和分布式规划。使用集中式计划,具有迷宫拓扑结构以及机器人初始位置和目标位置的全部知识的单个处理器可以使用标准图形搜索技术确定最佳时间路径。中央计划器提供了最佳的解决方案(无论何时存在),但是它们在计算上是令人望而却步的-图形搜索的复杂度随着导航机器人的数量呈指数增长。在分布式计划版本中,首先分别为每个机器人计算最小长度路径(即,在假设每个机器人独自导航迷宫的情况下)。然后,对局部最优路径进行局部修改以产生无碰撞的路径。我们的修改基于对机器人可见区域的限制-机器人在“看到”附近的其他机器人并意识到碰撞的威胁时会调整其独立计划的路径。在极限情况下,机器人完全是“盲人”的,他们通过与其他机器人碰撞来了解其他机器人的存在。

著录项

  • 作者

    Lebaudy, Andres.;

  • 作者单位

    Drexel University.;

  • 授予单位 Drexel University.;
  • 学科 Engineering Electronics and Electrical.;Computer Science.;Engineering System Science.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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