@workshop{Latif2023d,
title = {CQLite: Coverage-biased Q-Learning for Communication-Efficient Multi-Robot Exploration},
author = {Ehsan Latif and Ramviyas Parasuraman},
url = {https://sites.google.com/view/camrs-icra2023/contributed-papers},
year = {2023},
date = {2023-06-02},
booktitle = {ICRA 2023 Workshop: Communication Challenges in Multi-Robot Systems: Perception, Coordination, and Learning},
abstract = {Multiple mobile robots must autonomously navigate and cooperatively explore complex environments in practical applications. Traditional methods face high communication and update costs of map merging. We propose CQLite, a distributed Q-learning-based approach with a coverage-weighted reward function and reduced communication overhead. CQLite's convergence and efficiency are analyzed theoretically and validated experimentally, outperforming RRT and DRL techniques with over 2x reduction in computation and communication.},
keywords = {cooperation, exploration, multi-robot},
pubstate = {published},
tppubtype = {workshop}
}