# Multi-Robot Active Information Gathering with Periodic Communication

## TL;DR

This paper presents a method for coordinating multi-robot teams in information gathering tasks with periodic communication, using decentralized decision-making techniques, validated through simulations and real-world experiments.

## Contribution

It introduces a novel approach applying Dec-POMDP techniques to multi-robot information gathering with periodic communication constraints.

## Key findings

- Decentralized decision-making improves information gathering efficiency.
- Simulation results show significant performance gains.
- Real-world target tracking validates the approach's feasibility.

## Abstract

A team of robots sharing a common goal can benefit from coordination of the activities of team members, helping the team to reach the goal more reliably or quickly. We address the problem of coordinating the actions of a team of robots with periodic communication capability executing an information gathering task. We cast the problem as a multi-agent optimal decision-making problem with an information theoretic objective function. We show that appropriate techniques for solving decentralized partially observable Markov decision processes (Dec-POMDPs) are applicable in such information gathering problems. We quantify the usefulness of coordinated information gathering through simulation studies, and demonstrate the feasibility of the method in a real-world target tracking domain.