最小化转弯的多机器人覆盖

Isaac Vandermeulen, Roderich Groß, A. Kolling
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引用次数: 14

摘要

多机器人覆盖问题是指几个相同的机器人规划路径,使机器人所跟踪的组合区域完全覆盖它们所处的环境。我们考虑多机器人覆盖问题,其目标是最小化任务时间,这取决于机器人所采取的回合数。为了解决这个问题,我们首先将环境划分为与机器人覆盖工具宽度相等的细长矩形。我们的新划分启发式算法产生了一组最小回合数的秩。接下来,我们在秩集上解决了一个变体的多重旅行销售人员问题(m-TSP),以最小化机器人的任务时间。最终的覆盖计划保证覆盖整个环境。我们使用25个室内环境的真实地图为机器人真空提供覆盖计划,并将解决方案与没有最小化转弯目标的路径规划进行比较。对于1-5个机器人的队伍来说,最小化回合数平均减少了6.7%的回合数和3.8%的覆盖时间。
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Turn-minimizing multirobot coverage
Multirobot coverage is the problem of planning paths for several identical robots such that the combined regions traced out by the robots completely cover their environment. We consider the problem of multirobot coverage with the objective of minimizing the mission time, which depends on the number of turns taken by the robots. To solve this problem, we first partition the environment into ranks which are long thin rectangles the width of the robot’s coverage tool. Our novel partitioning heuristic produces a set of ranks which minimizes the number of turns. Next, we solve a variant of the multiple travelling salesperson problem (m-TSP) on the set of ranks to minimize the robots’ mission time. The resulting coverage plan is guaranteed to cover the entire environment. We present coverage plans for a robotic vacuum using real maps of 25 indoor environments and compare the solutions to paths planned without the objective of minimizing turns. Turn minimization reduced the number of turns by 6.7% and coverage time by 3.8% on average for teams of 1–5 robots.
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