  We study the problem of assigning robots with actions to track targets. The
objective is to optimize the robot team's tracking quality which can be defined
as the reduction in the uncertainty of the targets' states. Specifically, we
consider two assignment problems given the different sensing capabilities of
the robots. In the first assignment problem, a single robot is sufficient to
track a target. To this end, we present a greedy algorithm (Algorithm 1) that
assigns a robot with its action to each target. We prove that the greedy
algorithm has a 1/2 approximation bound and runs in polynomial time. Then, we
study the second assignment problem where two robots are necessary to track a
target. We design another greedy algorithm (Algorithm 2) that assigns a pair of
robots with their actions to each target. We prove that the greedy algorithm
achieves a 1/3 approximation bound and has a polynomial running time. Moreover,
we illustrate the performance of the two greedy algorithms in the ROS-Gazebo
environment where the tracking patterns of one robot following one target using
Algorithm 1 and two robots following one target using Algorithm 2 are clearly
observed. Further, we conduct extensive comparisons to demonstrate that the two
greedy algorithms perform close to their optimal counterparts and much better
than their respective (1/2 and 1/3) approximation bounds.
