Information correlated Levy walk exploration and distributed mapping using a swarm of robots
This work addresses the challenge of distributed mapping in robotics for applications like search and rescue or environmental monitoring, but it is incremental as it builds on existing consensus and topological data analysis methods.
The authors tackled the problem of constructing an occupancy grid map of an unknown environment using a swarm of robots with limited communication, by having robots perform Levy walks guided by mutual information to maximize exploration efficiency, and proved that all robots' maps converge to the actual map through consensus algorithms.
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.