Research on Active Firefighting Robot Navigation Based on the Improved AUKF Algorithm

DOI

10.2991/978-94-6463-222-4_9How to use a DOI?

Keywords

Autonomous Movement; Fusion Localization; Adaptive Unscented Kalman Filtering; Robot Navigation

Abstract

It is difficult for autonomous mobile robots to rely on a single positioning method to obtain accurate pose information in complex indoor environments, so the real-time pose of the robot is generally obtained through multi-source fusion positioning during navigation. However, in the fusion localization algorithm based on AUKF (Adaptive Unscented Kalman Filtering), the Sage-Husa noise filter, which updates the white noise covariance of the random variable and the observed variable, is easy to cause the random variable system white noise covariance to lose non-negativity or the observed variable system to lose non-negativity. The white noise covariance loses its positive definiteness, which causes the divergence of the AUKF filtering algorithm and reduces the fusion accuracy. In order to solve the above problems, an improved AUKF algorithm is proposed that incorporates the covariance correction factor Roth, thereby improving the positive definiteness of the algorithm variance as well as the positioning accuracy of the fusion algorithm. Experimental results show that the improved AUKF algorithm achieves an average positioning accuracy of 95.23% in the x-axis direction, 94.06% in the y-axis direction, and 97.13% in the heading angle of the robot navigation coordinate system. It meets the requirements for accurate pose perception for autonomous mobile robot navigation in indoor environments.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hubin Du
AU  - Qiuyu Li
AU  - Tanglong Chen
AU  - Yongtao Liu
AU  - Hengyuan Zhang
AU  - Ziqian Guan
PY  - 2023
DA  - 2023/08/28
TI  - Research on Active Firefighting Robot Navigation Based on the Improved AUKF Algorithm
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 96
EP  - 105
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_9
DO  - 10.2991/978-94-6463-222-4_9
ID  - Du2023
ER  -