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Article published In: Human Robot Collaborative Intelligence: Theory and applications
Edited by Chenguang Yang, Xiaofeng Liu, Junpei Zhong and Angelo Cangelosi
[Interaction Studies 20:1] 2019
► pp. 78–101

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An improved measurement variable estimation model for positioning mobile robot

 | Xi’an University of Posts and Telecommunications
 | Xi’an University of Posts and Telecommunications
 | Xi’an University of Posts and Telecommunications
 | Xi’an University of Posts and Telecommunications
 | Xi’an University of Posts and Telecommunications
 | Multimedia Computing, Communications, and Artificial Intelligence Research Federation University Australia
Published online: 15 July 2019
DOI logo with link
https://doi.org/10.1075/is.18014.qu
Abstract
The localization and navigation technology are the key factors in the research of mobile robots. With the demand of smart manufacturing industry and the development of robotics technology, the importance of mobile robot has become increasingly prominent. Mobile robot positioning research is mostly based on odometry, however, it has cumulative errors that would affect the accuracy of positioning results.
This paper describes an improved measurement model that suitable from 0° to 180° and used this model in the Extended Kalman Filter (EKF) and Unscented Kalman Filter(UKF) time update step respectively, the method can address the interference of kinematics model predicted position and heading angle, both of them are easily disturbed by noises and other factors. Designing a tracked mobile robot as experimental platform to collect the raw data, conducting experimental research including the performance of hardware platform and autonomous obstacle avoidance, the real-time and stability of remote data interaction, and the accuracy of optimal pose estimation. The simulation results have been verified the accuracy of the improved measurement model applied to UKF.
Article outline
  • I.Introduction
  • II.Mobile robot model
    • A.Kinematics model
    • B.Measurement model
  • III.Filter for nonlinear system
    • A.Extended Kalman Filter
    • B.Unscented Kalman Filter
  • IV.Experimental verification
    • A.Experiment platform
    • B.Indoor experimental results
    • C.Outdoor experimental results
    • D.Experimental results analysis
  • V.Conclusions
  • References
References (19)
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