Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM. (October 2016)

• Record Type: Journal Article
• Authors: Chen, Xiyuan; Xu, Yuan; Li, Qinghua; Tang, Jian; Shen, Chong
• Is Part Of: Measurement. Volume 92(2016)
• Page Start: 243
• Page End: 251
• Publication Date: 2016-10
• Subjects: INS -- Integration navigation -- EKF -- LS-SVM -- Ultrasonic positioning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.06.025
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An integrated navigation using INS and ultrasonic positioning was proposed. An integration algorithm using EKF and LS-SVM was proposed. A real integrated navigation system was developed. Several real indoor tests are implemented to assess the performance. Comparison between the proposed method and Chiang-Huang-ANN method is provided. Abstract: The ultrasonic positioning system is able to provide centimeter-level location information. However, the signal of the system is easy to be disturbed and the outages of the positioning system appear. Inertial measuring units (IMUs) is a self-contained device and can provide long-term navigation information independently, but it has the drawback of error drift. In order to obtain accurate and continuous location information indoors for indoor mobile robots, this work proposed a seamless integrated navigation utilizing extended Kalman filter (EKF) and Least Squares Support Vector Machine (LS-SVM). In this mode, the EKF estimates the position and the velocity of the robot while the signals of ultrasonic positioning system are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding filter states. Once the signals of ultrasonic positioning system are outages, the model is able to correct inertial navigation system (INS) solution as filter does. A prototype of the system has been worked in a real scenario. The results show that the performance of EKF is robust, and the prediction of LS-SVM is able to work as EKF does during the outages.