GMC-Pos: Graph-Based Multi-Robot Coverage Positioning Method
Abstract
Nowadays, several real-world tasks require adequate environment coverage for maintaining communication between multiple robots, for example, target search tasks, environmental monitoring, and post-disaster rescues. In this study, we look into a situation where there are a human operator and multiple robots, and we assume that each human or robot covers a certain range of areas. We want them to maximize their area of coverage collectively. Therefore, in this paper, we propose the Graph-Based Multi-Robot Coverage Positioning Method (GMC-Pos) to find strategic positions for robots that maximize the area coverage. Our novel approach consists of two main modules: graph generation and node selection. Firstly, graph generation represents the environment using a weighted connected graph. Then, we present a novel generalized graph-based distance and utilize it together with the graph degrees to be the conditions for node selection in a recursive manner. Our method is deployed in three environments with different settings. The results show that it outperforms the benchmark method by 15.13% to 24.88% regarding the area coverage percentage.
Cite
@article{arxiv.2310.11805,
title = {GMC-Pos: Graph-Based Multi-Robot Coverage Positioning Method},
author = {Khattiya Pongsirijinda and Zhiqiang Cao and Muhammad Shalihan and Benny Kai Kiat Ng and Billy Pik Lik Lau and Chau Yuen and U-Xuan Tan},
journal= {arXiv preprint arXiv:2310.11805},
year = {2023}
}
Comments
This paper has been accepted by the 2023 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2023)