IRIDIA - Supplementary Information (ISSN: 2684-2041)

Supplementary material for the paper:

Random walk exploration for swarm mapping

Miquel Kegeleirs, David Garzón Ramos, and Mauro Birattari (February 2019)


Table of Contents
  1. Abstract
  2. Dataset
  3. Videos
    1. Ballistic motion
    2. Brownian motion
    3. Correlated random walk
    4. Lévy taxis
    5. Lévy walk

Abstract

Research in swarm robotics has shown that robot swarms are effective in the exploration of unknown environments. However, little work has been devoted to port the exploration capabilities of robot swarms into the context of mapping. Indeed, conceiving robot swarms that can map an unknown environment in a robust, scalable, and flexible way is an open issue. In this paper, we investigate a swarm mapping method in which robots first individually map the environment by random walk and then, we merge their maps into a single, global one. We focus on comparing the quality of maps produced by swarms that explore by using five variants of random walk. Our experiments with ten e-puck robots show that, despite the individual maps are incomplete by themselves, it is possible to collectively map the environment by merging them. We found that the quality of the map depends on the exploration behavior of the individuals. Our results suggest that one of the variants of random walk, the ballistic motion, gives better mapping results for closed environments.

Dataset

Individual and global maps produced with swarm mapping are available for download. The control software, and ARGoS and ROS configuration files are available for download too.

Videos

You can find below videos that illustrate the simulated and real robot behavior for each random walk variant.

Ballistic motion

Simulation

Reality

Brownian motion

Simulation

Reality

Correlated random walk

Simulation

Reality

Lévy taxis

Simulation

Reality

Lévy walk

Simulation

Reality