Probably approximately correct coverage for robots with uncertainty
Probably approximately correct coverage for robots with uncertainty
复制标题
对于具有不确定性的机器人，可能大致正确的覆盖范围
DOI：
10.1109/iros.2011.6094695
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
T. Bretl
中科院分区：
文献类型：
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作者：
C. Das;Aaron T. Becker;T. Bretl
机器人覆盖的经典问题是规划一条路径，使机器人上的一个点在自由空间中的每个点的固定距离内。在传感和驱动存在显著不确定性的情况下，可能不再可能保证机器人始终覆盖所有自由空间，因此我们试图解决的问题变得不清楚。我们将通过采用“可能近似正确”的性能度量来恢复清晰度，该度量捕获覆盖自由空间的分数1-δ的概率1-ε。具有不确定性的机器人的覆盖问题是规划一个反馈策略，以实现给定的ε和δ值。就像经典问题的解决方案是由最终的路径长度来判断一样，我们问题的解决方案是由所需的执行时间来判断的。我们将通过将其应用于模拟中的几个例子来展示我们的性能指标的实际效用。
The classical problem of robot coverage is to plan a path that brings a point on the robot within a fixed distance of every point in the free space. In the presence of significant uncertainty in sensing and actuation, it may no longer be possible to guarantee that the robot covers all of the free space all the time, and so it becomes unclear what problem we are trying to solve. We will restore clarity by adopting a “probably approximately correct” measure of performance that captures the probability 1 − ε of covering a fraction 1 − δ of the free space. The problem of coverage for a robot with uncertainty is then to plan a feedback policy that achieves a given value of ε and δ. Just as solutions to the classical problem are judged by the resulting path length, solutions to our problem are judged by the required execution time. We will show the practical utility of our performance measure by applying it to several examples in simulation.