Consider search on an infinite line involving an autonomous robot starting at the origin of the line and an oblivious moving target at initial distance $d \geq 1$ from it. The robot can change direction and move anywhere on the line with constant maximum speed $1$ while the target is also moving on the line with constant speed $v>0$ but is unable to change its speed or direction. The goal is for the robot to catch up to the target in as little time as possible. The classic case where $v=0$ and the target's initial distance $d$ is unknown to the robot is the well-studied ``cow-path problem''. Alpert and Gal gave an optimal algorithm for the case where a target with unknown initial distance $d$ is moving away from the robot with a known speed $v<1$. In this paper we design and analyze search algorithms for the remaining possible knowledge situations, namely, when $d$ and $v$ are known, when $v$ is known but $d$ is unknown, when $d$ is known but $v$ is unknown, and when both $v$ and $d$ are unknown. Furthermore, for each of these knowledge models we consider separately the case where the target is moving away from the origin and the case where it is moving toward the origin. We design algorithms and analyze competitive ratios for all eight cases above. The resulting competitive ratios are shown to be optimal when the target is moving towards the origin as well as when $v$ is known and the target is moving away from the origin.
翻译:考虑在一条无限的线上搜索, 涉及自动机器人, 从直线的起源开始, 且在初始距离 $d\ geq 1$ 上, 不明的移动目标 。 机器人可以改变方向, 以恒定最大速度移动 $1美元, 而目标也以恒定速度移动在线上 $v>0 美元, 但无法改变速度或方向 。 目标是让机器人在尽可能短的时间内赶上目标 。 典型的情况是, 机器人不知道$v=0美元和目标最初距离 $d$。 机器人不知道的“ coow- path proble 问题 ” 。 Alpert 和 Gal 给出了一个最佳算法, 当初始距离不明的目标正在移动, $d$ 正在移动, 并且 当我们知道最优的起源 时, 将 美元 指向最优的路径移动 。 当我们知道 和最优的路径 时, 将显示我们最接近最优的路径 。