This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods.

本文针对复杂环境下的多机器人主动信息获取任务，提出了一种新颖的、高度可扩展的基于采样的规划算法。主动信息收集场景包括目标定位和跟踪、主动 SLAM、监视、环境监测等。目标是计算传感机器人的控制策略，使动态隐藏状态在先验上的累积不确定性最小化。未知的地平线。为了解决这个问题，我们提出了一种新的基于采样的算法，它同时探索机器人运动空间和可达信息空间。与相关的基于采样的方法不同，我们表明所提出的算法在概率上是完整的、渐近最优的，并且受到收敛速度界限的支持。此外，我们提出了一种新颖的有偏采样策略，该策略将探索偏向于信息丰富的区域。这使得所提出的方法能够快速计算传感器策略，从而在可能涉及大型传感器团队、工作空间和隐藏状态维度的大规模估计任务中实现所需的不确定性水平。讨论了所提出算法的扩展，以解决没有先验信息的隐藏状态。