Fault-tolerant pattern formation by multiple robots: A learning approach

Jia Wang, Jiannong Cao, Shan Jiang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 36th International Symposium on Reliable Distributed Systems, SRDS 2017
PublisherIEEE Computer Society
Number of pages2
ISBN (Electronic)9781538616796
Publication statusPublished - 13 Oct 2017
Event36th IEEE International Symposium on Reliable Distributed Systems, SRDS 2017 - Hong Kong, Hong Kong
Duration: 26 Sep 201729 Sep 2017

Publication series

NameProceedings of the IEEE Symposium on Reliable Distributed Systems
ISSN (Print)1060-9857


Conference36th IEEE International Symposium on Reliable Distributed Systems, SRDS 2017
Country/TerritoryHong Kong
CityHong Kong


  • Fault-tolerant
  • Multi-roobt system
  • Pattern formation
  • Reinforcement learning

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications

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