Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Learning based multi-robot coverage algorithm

Mingzhe Song * 1
1 Fuzhou University

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 113-122
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Mingzhe Song. Learning based multi-robot coverage algorithm. ACE (2024) Vol. 52: 113-122. DOI: 10.54254/2755-2721/52/20241446.

Abstract

Multi-robot coverage algorithm is essential in exploration, search and rescue, tracking and other tasks. Nowadays, global planning-based approaches are difficult to solve the actual deployments of very large robot team coverage problems. In this article we use the heuristic algorithm based on graph neural networks to solve the multi robot coverage algorithm. Firstly, we discretize the coverage task and encode it into a graph. The location of graph and the robots are nodes. Then we design a graph neural network controller and use imitation methods to train the controller. The controller will generate the solution that is not inferior to the expert through imitating an open-loop expert solution based on VPR. Finally, we designed a graph neural network architecture to perform zero shot generalization on large maps and teams, enabling the system to be extended to larger map teams. It is difficult for the expert. And we successfully use this model to simulate 10 quadcopter and a number of buildings in a city. We also prove the GNN controller is better than the method based on the planning in the exploration task.

Keywords

multi-robot, coverage, graph neural networks

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241446
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated