# Information correlated Levy walk exploration and distributed mapping using a swarm of robots

## TL;DR

This paper introduces a distributed multi-robot exploration method using Levy walks and mutual information maximization to efficiently map unknown environments, with proven convergence and validated through simulations and experiments.

## Contribution

It presents a novel distributed approach combining Levy walk exploration, mutual information, and topological data analysis for multi-robot mapping with convergence guarantees.

## Key findings

- All robots' maps converge to the actual environment map.
- The method is effective in 2D simulations and real experiments.
- Topological data analysis enhances occupancy grid thresholding.

## Abstract

In this work, we present a novel distributed method for constructing an occupancy grid map of an unknown environment using a swarm of robots with global localization capabilities and limited inter-robot communication. The robots explore the domain by performing Levy walks in which their headings are defined by maximizing the mutual information between the robot's estimate of its environment in the form of an occupancy grid map and the distance measurements that it is likely to obtain when it moves in that direction. Each robot is equipped with laser range sensors, and it builds its occupancy grid map by repeatedly combining its own distance measurements with map information that is broadcast by neighboring robots. Using results on average consensus over time-varying graph topologies, we prove that all robots' maps will eventually converge to the actual map of the environment. In addition, we demonstrate that a technique based on topological data analysis, developed in our previous work for generating topological maps, can be readily extended for adaptive thresholding of occupancy grid maps. We validate the effectiveness of our distributed exploration and mapping strategy through a series of 2D simulations and multi-robot experiments.