High-Precision Positioning Method for Robot Acoustic Ranging Based on Self-Optimization of Base Stations
Source abstract
Abstract
In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Underwater Vehicles and Communication Systems · Inertial Sensor and Navigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai 200240, China
Funding
Shanghai Agriculture Applied Technology Development Program
References (17)
Show source-provided references
1. Li, S., Chang, X., Yang, C., Jiang, K., Wang, Z., Wang, L., and Li, X. (2018, January 14–16). A Fast Vehicle Horn Sound Location Method with Improved SRP-PHAT. Proceedings of the 2018 IEEE International Conference on Progress in Informatics and Computing (PIC), Suzhou, China.10.1109/PIC.2018.8706325 ↗
2. Dibiase, J.H. (2000). A High-Accuracy, Low-Latency Technique for Talker Localization in Reverberant Environments. [Ph.D. Thesis, Brown University].
3. Sieriebriakov, A., Hospodarchuk, O., Lakhtyr, D., Simakhin, V., and Bondar, S. (2023, January 19–21). Technology of sound source localization based on incoming sound intensity. Proceedings of the 2023 IEEE 18th International Conference on Computer Science and Information Technologies (CSIT), Lviv, Ukraine.10.1109/CSIT61576.2023.10324229 ↗
4. Wang, J., Guo, Y., Lu, S., Zhang, Y., and Shu, H. (2024, January 25–27). An Acoustic Indoor Localization Method Based on Directional Variability for Mobile Robot. Proceedings of the 2024 IEEE International Conference on Industrial Technology (ICIT), Bristol, UK.10.1109/ICIT58233.2024.10540970 ↗
5. A TOA-Based Localization Algorithm With Simultaneous NLOS Mitigation and Synchronization Error Elimination10.1109/LSENS.2019.2955125 ↗
6. Tong, K., Wang, X., Khabbazibasmenj, A., and Dounavis, A. (2015, January 8–12). Optimum reference node deployment for TOA-based localization. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.10.1109/ICC.2015.7248825 ↗
7. Zhu, K., Wei, Y., and Xu, R. (2016, January 10–13). TOA-based localization error modeling of distributed MIMO radar for positioning accuracy enhancement. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.10.1109/RADAR.2016.8059316 ↗
8. Koohzadi, M., Ebadollahi, S., Vahidnia, R., and Dian, F.J. (2019, January 17–19). Implementation and Comparison of Different Tropospheric Models to Reduce Error Low-cost Real-time GPS Positioning. Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.10.1109/IEMCON.2019.8936204 ↗
9. Yan, Z., Wan-Fang, Z., and Li-Li, C. (2022, January 26–27). Design of Robot Positioning System Based on the Integration of Slam System and Inertial System. Proceedings of the 2022 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Hengyang, China.10.1109/ICITBS55627.2022.00151 ↗
10. Wu, F., and Liu, Z. (2020, January 11–12). Research on UWB/IMU Fusion Positioning Technology in Mine. Proceedings of the 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos.10.1109/ICITBS49701.2020.00207 ↗
11. Zhang, Z., Han, X., Shi, Y., Wang, Y., Ma, L., and Yin, J. (2024, January 29–31). Acoustic Positioning System Beneath the Arctic Ice and Its Experimental Verification. Proceedings of the 2024 OES China Ocean Acoustics (COA), Harbin, China.10.1109/COA58979.2024.10723378 ↗
12. Goldoni, E., Savioli, A., Risi, M., and Gamba, P. (2010, January 12–15). Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4. Proceedings of the 2010 European Wireless Conference (EW), Lucca, Italy.10.1109/EW.2010.5483396 ↗
13. A robust approach for WSN localization for underground coal mine monitoring using improved RSSI technique10.18280/mmep.050314 ↗
14. Zhou, Y., Qiu, T., Xia, F., and Hou, G. (2011, January 19–22). N-Times Trilateral Centroid Weighted Localization Algorithm of Wireless Sensor Networks. Proceedings of the Internet of Things (iThings/CPSCom), 2011 International Conference on and 4th International Conference on Cyber, Physical and Social Computing, Dalian, China.10.1109/iThings/CPSCom.2011.23 ↗
15. Fang, H., and Li, Y. (2019). Wireless Basestation Location Algorithm based on Clustering and TDOA. Commun. Technol., 311–317.
16. A Wireless Location Method with High Precision for Underground Personnel Tracking
17. Zu, M., and Rong, X. (2018). UWB Indoor Localization Algorithm Based on Inner Triangle Centroid Correction and Taylor. Nat. Sci. J. Harbin Norm. Univ., 42–47.