Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization
Abstract
We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.
Cite
@article{arxiv.2403.04021,
title = {Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization},
author = {Yewei Huang and Xi Lin and Brendan Englot},
journal= {arXiv preprint arXiv:2403.04021},
year = {2024}
}