Technical Report: Scalable Active Information Acquisition for Multi-Robot Systems
This addresses the scalability issue in multi-robot systems for applications like environmental monitoring, though it is incremental as it builds on existing AIA methods with a focus on decentralization.
The paper tackles the problem of scaling multi-robot active information acquisition tasks, such as target localization and surveillance, by proposing a decentralized algorithm that decomposes tasks into local subtasks with role switching, enabling it to handle large-scale scenarios where centralized methods fail computationally.
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.