@inproceedings{b3cdaf82ba52406a9499c231f5fc9f7d,
title = "Fault-tolerant pattern formation by multiple robots: A learning approach",
abstract = "In the field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab.",
keywords = "Fault-tolerant, Multi-roobt system, Pattern formation, Reinforcement learning",
author = "Jia Wang and Jiannong Cao and Shan Jiang",
year = "2017",
month = oct,
day = "13",
doi = "10.1109/SRDS.2017.42",
language = "English",
series = "Proceedings of the IEEE Symposium on Reliable Distributed Systems",
publisher = "IEEE Computer Society",
pages = "268--269",
booktitle = "Proceedings - 2017 IEEE 36th International Symposium on Reliable Distributed Systems, SRDS 2017",
address = "United States",
note = "36th IEEE International Symposium on Reliable Distributed Systems, SRDS 2017 ; Conference date: 26-09-2017 Through 29-09-2017",
}