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Graph rigidity and localization of multi-robot formations.

Fan Zhang1

  • 1General Robotics Automation Sensing and Perception Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA. zhangfan@grasp.cis.upenn.edu

Journal of Zhejiang University. Science
|April 15, 2004
PubMed
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This study establishes conditions for multi-robot localization using distributed sensors and graph rigidity. A weighted least-squares method estimates localization quality with noisy data, demonstrated on five car-like robots.

Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Multi-robot systems require accurate localization for coordinated tasks.
  • Distributed sensor networks and graph theory offer robust localization solutions.
  • Estimating localization quality is crucial for reliable performance.

Purpose of the Study:

  • To provide a theoretical foundation for multi-robot formation localization.
  • To establish sufficient and necessary conditions for complete localization in SE(2).
  • To present a method for estimating localization quality under noisy conditions.

Main Methods:

  • Utilizing graph rigidity theory for formation analysis.
  • Applying distributed sensor networks for data acquisition.

Related Experiment Videos

  • Implementing a linearized weighted least-squares algorithm for quality estimation.

    • Main Results:

    • Sufficient and necessary conditions for complete localization in SE(2) were derived.
    • A practical method for assessing localization accuracy with incomplete and noisy data was developed.
    • The approach was successfully validated on a team of five car-like robots.

    Conclusions:

    • The proposed theoretical framework enables robust multi-robot formation localization.
    • The developed algorithm effectively estimates localization quality in real-world scenarios.
    • This research contributes to the advancement of autonomous multi-robot coordination.