技术报告：多机器人系统的可扩展主动信息采集
Technical Report: Scalable Active Information Acquisition for Multi-Robot Systems
摘要：
针对多机器人主动信息获取（AIA）任务，提出了一种新的高度可扩展的非近视规划算法。AIA场景包括目标定位和跟踪、主动SLAM、监视、环境监测等。我们的目标是计算多个机器人的控制策略，最大限度地减少累积的不确定性的静态隐藏状态在一个先验未知的地平线。大多数现有的AIA方法都是集中式的，因此面临扩展挑战。为了缓解这个问题，我们提出了一个在线算法，依赖于通过动态空间划分方法分解AIA任务到本地任务。本地子任务是在线制定的，并要求机器人根据其在环境中的功能在探索和主动信息收集角色之间切换。切换过程与优化信息收集紧密集成，从而产生混合控制方法。我们表明，所提出的基于分解的算法是概率完成均匀传感器团队和线性和高斯假设下。我们提供了大量的模拟结果表明，该算法可以解决大规模的估计任务，计算上具有挑战性的解决使用现有的集中式方法。
英文摘要：
This paper proposes a novel highly scalable non-myopic planning algorithm for multi-robot Active Information Acquisition (AIA) tasks. AIA scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for multiple robots which minimize the accumulated uncertainty of a static hidden state over an a priori unknown horizon. The majority of existing AIA approaches are centralized and, therefore, face scaling challenges. To mitigate this issue, we propose an online algorithm that relies on decomposing the AIA task into local tasks via a dynamic space-partitioning method. The local subtasks are formulated online and require the robots to switch between exploration and active information gathering roles depending on their functionality in the environment. The switching process is tightly integrated with optimizing information gathering giving rise to a hybrid control approach. We show that the proposed decomposition-based algorithm is probabilistically complete for homogeneous sensor teams and under linearity and Gaussian assumptions. We provide extensive simulation results that show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging to solve using existing centralized approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
