Competitive Analysis of Repeated Greedy Auction Algorithm for Online Multi-Robot Task Assignment

Abstract

We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a competitive ratio of 1 over 3. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.