A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems

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PeerJ Computer Science

Main article text

 

Introduction

  • (1) An approximation method based on particles is proposed to account for the uncertainty of the positions of robots. This process can improve the accuracy of the extended Kalman filter in positioning.

  • (2) A cooperative localization algorithm based on the CI method is proposed to achieve localization by fusing the preliminary estimations. And the advantage of this algorithm is that it can achieve high localization accuracy by minimizing the trace of the fused estimation covariance.

  • (3) The KLD theory is used to eliminate the abnormal measurement and provides a robust estimation for robot.

Preliminary work

Kinematic model of robot

Measurement model

Method for considering measurement uncertainty

Cooperative localization

State prediction process

Measurement update process

Remark 1. Actually, the calculation method of Rk+1Ri,Rj is the main difference between Eq. (12) and the extended Kalman filter without considering robots’ uncertainty. And in Eq. (12), the calculation of Rk+1Ri,Rj have considered the uncertainty of robots’ positions. Moreover, the first term of Eq. (12) has considered the uncertainly of robot Ri’s position, so it’s only need to consider the uncertainly of robot Rj’s position when calculating the value of Rk+1Ri,Rj. In other words, it is only need to generate a group particles in application to approximate the probability distribution of Rj’s position, which is also a method to reduce the computation load.

Strategy for eliminating abnormal measurements

Simulations

Validating the method of considering robots’ uncertainty

Simulation for the case with normal measurement

Simulation for the case with abnormal measurement

Conclusions

Supplemental Information

Raw data for Figures 3, 4, 6, 7, 8 and 9.

DOI: 10.7717/peerj-cs.1373/supp-1

All codes for the simulations and figures.

The “figure” file folder contains the codes for plotting the illustration of covariance intersection. The “path” file folder contains the codes for plotting all simulation figures.

DOI: 10.7717/peerj-cs.1373/supp-2

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Miao Wang performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, and approved the final draft.

Qingshan Liu conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The code is available in the Supplemental File.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 62276062. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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