多维在线机器人运动
Multidimensional Online Robot Motion
摘要：
我们考虑三个相关的问题，机器人运动在任意维度：覆盖，搜索和导航。对于每个问题，要求球形机器人在未知环境中完成与运动相关的任务，该环境的几何形状由机器人在导航过程中学习。机器人被假定有触觉和全球定位传感器。我们认为这些问题的角度（非线性）的竞争力所定义的加布里埃尔和里蒙。我们首先表明，在3维和更高的维度上，竞争力没有上限：每一个在线算法都可以做得比最优算法差。然后，我们修改的问题，假设一个固定的间隙参数。在此假设下，我们能够给出最优竞争算法。
英文摘要：
We consider three related problems of robot movement in arbitrary dimensions: coverage, search, and navigation. For each problem, a spherical robot is asked to accomplish a motion-related task in an unknown environment whose geometry is learned by the robot during navigation. The robot is assumed to have tactile and global positioning sensors. We view these problems from the perspective of (non-linear) competitiveness as defined by Gabriely and Rimon. We first show that in 3 dimensions and higher, there is no upper bound on competitiveness: every online algorithm can do arbitrarily badly compared to the optimal. We then modify the problems by assuming a fixed clearance parameter. We are able to give optimally competitive algorithms under this assumption.
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