Technical Report: Distributed Sampling-based Planning for Non-Myopic Active Information Gathering

TL;DR

This paper introduces a distributed sampling-based planning algorithm for multi-robot active information gathering, enabling scalable, efficient, and decentralized reduction of environmental uncertainty in complex scenarios.

Contribution

It presents a novel distributed non-myopic planning algorithm that improves scalability and reduces computational costs for multi-robot information gathering tasks.

Findings

Algorithm is probabilistically complete and asymptotically optimal.

Demonstrates scalability to large multi-robot systems.

Outperforms centralized methods in computational efficiency.

Abstract

This paper addresses the problem of active information gathering for multi-robot systems. Specifically, we consider scenarios where robots are tasked with reducing uncertainty of dynamical hidden states evolving in complex environments. The majority of existing information gathering approaches are centralized and, therefore, they cannot be applied to distributed robot teams where communication to a central user is not available. To address this challenge, we propose a novel distributed sampling-based planning algorithm that can significantly increase robot and target scalability while decreasing computational cost. In our non-myopic approach, all robots build in parallel local trees exploring the information space and their corresponding motion space. As the robots construct their respective local trees, they communicate with their neighbors to exchange and aggregate their local beliefs about the hidden state through a distributed Kalman filter. We show that the proposed algorithm is probabilistically complete and asymptotically optimal. We provide extensive simulation results that demonstrate the scalability of the proposed algorithm and that it can address large-scale, multi-robot information gathering tasks, that are computationally challenging for centralized methods.