Submodular maximization has been widely used in many multi-robot task planning problems including information gathering, exploration, and target tracking. However, the interplay between submodular maximization and communication is rarely explored in the multi-robot setting. In many cases, maximizing the submodular objective may drive the robots in a way so as to disconnect the communication network. Driven by such observations, in this paper, we consider the problem of maximizing submodular function with connectivity constraints. Specifically, we propose a problem called Communication-aware Submodular Maximization (CSM), in which communication maintenance and submodular maximization are jointly considered in the decision-making process. One heuristic algorithm that consists of two stages, i.e. \textit{topology generation} and \textit{deviation minimization} is proposed. We validate the formulation and algorithm through numerical simulation. We find that our algorithm on average suffers only slightly performance decrease compared to the pure greedy strategy.
翻译:许多多机器人任务规划问题,包括信息收集、探索和目标跟踪,都广泛使用了子模块最大化。然而,在多机器人环境中,很少探讨子模块最大化和通信之间的相互作用。在许多情况下,使子模块目标最大化可能会驱动机器人切断通信网络。在这种观察的驱动下,我们在本文件中审议了在连通性制约下最大限度地发挥子模块功能的问题。具体地说,我们提出了一个问题,即通信觉悟子模块最大化(CSM),在其中,通信维护和子模块最大化在决策过程中得到共同考虑。提出了一种由两个阶段组成的超常算法,即\ textit{tototology centry} 和\textit{ demination 最小化}。我们通过数字模拟验证了配方和算法。我们发现,与纯贪婪战略相比,我们的算法在平均上只受到轻微的性能下降。