Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter
 
research article

A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter

Wasik, Alicja  
•
Lima, Pedro U.
•
Martinoli, Alcherio  
2020
Autonomous Robots

This paper presents a strategy for providing reliable state estimates that allow a group of robots to realize a formation even when communication fails and the tracking data alone is insufficient for maintaining a stable formation. Furthermore, the tracking information does not provide the identity of the robot, therefore a simple fusion of tracking and communication data is not possible. We extend a Gaussian mixture probability hypothesis density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Our method of combining communicated data, information about the formation and sensory detections is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication is sporadic or suffers from short-term outage. The proposed method is validated using a high-fidelity simulator in scenarios with a formation of up to five robots. The results show that the proposed tracking strategy allows for sustaining formations in cluttered environments, with high measurement uncertainty and low quality communication.

  • Files
  • Details
  • Metrics
Type
research article
DOI
10.1007/s10514-019-09860-5
Author(s)
Wasik, Alicja  
Lima, Pedro U.
Martinoli, Alcherio  
Date Issued

2020

Published in
Autonomous Robots
Volume

44

Start page

395

End page

414

Subjects

Multi-robot tracking

•

Formation control

•

Cooperative positioning system

•

Probability hypothesis density filter

•

Cooperative localization

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DISAL  
Available on Infoscience
June 25, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/158530
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés