Technical Report: Scalable Active Information Acquisition for Multi-Robot Systems

TL;DR

This paper introduces a scalable, decentralized planning algorithm for multi-robot active information acquisition tasks, enabling large-scale, efficient, and adaptive environment monitoring and target tracking.

Contribution

It presents a novel online decomposition-based algorithm that improves scalability and efficiency for multi-robot AIA by local task formulation and hybrid control strategies.

Findings

Algorithm is probabilistically complete for homogeneous sensor teams.

Extensive simulations demonstrate effectiveness on large-scale estimation tasks.

Outperforms existing centralized approaches in scalability and computational efficiency.

Abstract

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.