Affiliation:
1. College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Abstract
With the continuous development of artificial intelligence technology, visible-light positioning (VLP) based on machine learning and deep learning algorithms has become a research hotspot for indoor positioning technology. To improve the accuracy of robot positioning, we established a three-dimensional (3D) positioning system of visible-light consisting of two LED lights and three photodetectors. In this system, three photodetectors are located on the robot’s head. We considered the impact of line-of-sight (LOS) and non-line-of-sight (NLOS) links on the received signals and used gated recurrent unit (GRU) neural networks to deal with nonlinearity in the system. To address the problem of poor stability during GRU network training, we used a learning rate attenuation strategy to improve the performance of the GRU network. The simulation results showed that the average positioning error of the system was 2.69 cm in a space of 4 m × 4 m × 3 m when only LOS links were considered and 2.66 cm when both LOS and NLOS links were considered with 95% of the positioning errors within 7.88 cm. For two-dimensional (2D) positioning with a fixed positioning height, 80% of the positioning error was within 9.87 cm. This showed that the system had a high anti-interference ability, could achieve centimeter-level positioning accuracy, and met the requirements of robot indoor positioning.
Funder
National Natural Science Fund Projects of China
Applied Technology Research and Development Fund Project of Inner Mongolia Autonomous Region
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics
Reference28 articles.
1. Survey of wireless indoor positioning techniques and systems;Liu;IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.),2007
2. Zhuang, Y., Yang, J., Li, Y., Qi, L., and El-Sheimy, N. (2016). Smartphone-based indoor localization with bluetooth low energy beacons. Sensors, 16.
3. Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements;Ruiz;IEEE Trans. Instrum. Meas.,2011
4. Yan, D., Kang, B., Zhong, H., and Wang, R. (2018, January 12–14). Research on positioning system based on Zigbee communication. Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.
5. UWB-based localization in large indoor scenarios: Optimized placement of anchor nodes;Monica;IEEE Trans. Aerosp. Electron. Syst.,2015
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献