Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Gong, Kun; Deng, Fang; Ma, Tao

doi:10.1109/ramech.2011.6070496

Cited by 1 publication

(3 citation statements)

References 6 publications

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“…Let us assume the value of k in the first experiment is 0.005 (reference the experience value in the study by Kun et al 30 ). The results of experiments are shown in Table 2, and the trajectory corresponding different k is shown in Figure 14.…”

Section: Fuzzy Algorithm For the Parameter K Of The Aekf Algorithmmentioning

confidence: 99%

“…Literature 30 has introduced a calibration method for electronic compass based on Fourier neural network trained by modified particle swarm optimization. The advantage of the calibration methods with respect to the studies by Kwon et al and Kun et al 29,30 is the error compensation of electronic compass's azimuth. But it is difficult to get the accurate value of electronic compass on any position of the robot motion path.…”

Section: Introductionmentioning

confidence: 99%

“…Kwon et al 29 propose a statistical calibration method to obtain optimal parameters dealing with magnetic interference and some necessary conditions for the earth's magnetic field (EMF) are derived to offer clues to correctness of compass data. Literature 30 has introduced a calibration method for electronic compass based on Fourier neural network trained by modified particle swarm optimization. The advantage of the calibration methods with respect to the studies by Kwon et al and Kun et al 29,30 is the error compensation of electronic compass's azimuth.…”

Section: Introductionmentioning

confidence: 99%

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An indoor mobile robot navigation technique using odometry and electronic compass

Chen,

Zhang

2017

International Journal of Advanced Robotic Systems

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A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.

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Section: Fuzzy Algorithm For the Parameter K Of The Aekf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An indoor mobile robot navigation technique using odometry and electronic compass

Chen,

Zhang

2017

International Journal of Advanced Robotic Systems

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show abstract

Mobile robot's electronic compass calibration based on modified Fourier Neural Network

Cited by 1 publication

References 6 publications

An indoor mobile robot navigation technique using odometry and electronic compass

An indoor mobile robot navigation technique using odometry and electronic compass

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