This paper studies the measurement scheduling problem for a group of N mobile robots moving on a flat surface that are preforming cooperative localization (CL). We consider a scenario in which due to the limited on-board resources such as battery life and communication bandwidth only a given number of relative measurements per robot are allowed at observation and update stage. Optimal selection of which teammates a robot should take a relative measurement from such that the updated joint localization uncertainty of the team is minimized is an NP-hard problem. In this paper, we propose a suboptimal greedy approach that allows each robot to choose its landmark robots locally in polynomial time. Our method, unlike the known results in the literature, does not assume full-observability of CL algorithm. Moreover, it does not require inter-robot communication at scheduling stage. That is, there is no need for the robots to collaborate to carry out the landmark robot selections. We discuss the application of our method in the context of an state-of-the-art decentralized CL algorithm and demonstrate its effectiveness through numerical simulations. Even though our solution does not come with rigorous performance guarantees, its low computational cost along with no communication requirement makes it an appealing solution for operatins with resource constrained robots.

中文翻译：

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资源受限条件下合作定位的测量调度

本文研究了一组 N 个移动机器人在平面上移动并执行协作定位 (CL) 的测量调度问题。我们考虑一种场景，由于电池寿命和通信带宽等机载资源有限，在观察和更新阶段只允许每个机器人进行给定数量的相对测量。机器人应该从哪些队友中进行相对测量以最小化团队更新后的关节定位不确定性的最佳选择是一个 NP-hard 问题。在本文中，我们提出了一种次优贪婪方法，允许每个机器人在多项式时间内在本地选择其地标机器人。与文献中的已知结果不同，我们的方法不假设 CL 算法的完全可观察性。而且，它在调度阶段不需要机器人间通信。也就是说，不需要机器人协作来进行地标机器人选择。我们讨论了我们的方法在最先进的分散 CL 算法的背景下的应用，并通过数值模拟证明了其有效性。尽管我们的解决方案没有严格的性能保证，但其低计算成本和无通信要求使其成为具有资源受限机器人的操作者的有吸引力的解决方案。