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We study the problem of assigning robots with actions to track targets. Theobjective is to optimize the robot team's tracking quality which can be definedas the reduction in the uncertainty of the targets' states. Specifically, weconsider two assignment problems given the different sensing capabilities ofthe robots. In the first assignment problem, a single robot is sufficient totrack a target. To this end, we present a greedy algorithm (Algorithm 1) thatassigns a robot with its action to each target. We prove that the greedyalgorithm has a 1/2 approximation bound and runs in polynomial time. Then, westudy the second assignment problem where two robots are necessary to track atarget. We design another greedy algorithm (Algorithm 2) that assigns a pair ofrobots with their actions to each target. We prove that the greedy algorithmachieves a 1/3 approximation bound and has a polynomial running time. Moreover,we illustrate the performance of the two greedy algorithms in the ROS-Gazeboenvironment where the tracking patterns of one robot following one target usingAlgorithm 1 and two robots following one target using Algorithm 2 are clearlyobserved. Further, we conduct extensive comparisons to demonstrate that the twogreedy algorithms perform close to their optimal counterparts and much betterthan their respective (1/2 and 1/3) approximation bounds.

Assignment Algorithms for Multi-Robot Multi-Target Tracking with Sufficient and Limited Sensing Capability