Please use this identifier to cite or link to this item:
Title: Fault-tolerant pattern formation by multiple robots : a learning approach
Authors: Wang, J 
Cao, J 
Jiang, S 
Keywords: Fault-tolerant
Multi-roobt system
Pattern formation
Reinforcement learning
Issue Date: 2017
Publisher: IEEE Computer Society
Source: Proceedings of the IEEE Symposium on Reliable Distributed Systems, 2017, v. 2017-September, 8069096, p. 268-269 How to cite?
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.
Description: 36th IEEE International Symposium on Reliable Distributed Systems, SRDS 2017, Hong Kong, China, 26-29 September, 2017
ISBN: 9781538616796
ISSN: 1060-9857
DOI: 10.1109/SRDS.2017.42
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Dec 8, 2018

Page view(s)

Citations as of Dec 10, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.