Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-19T11:33:42.501Z Has data issue: false hasContentIssue false
  • English
  • Français

A dynamic localization algorithm for a high-speed mobile robot using indoor GPS

Published online by Cambridge University Press:  08 August 2011

Seungkeun Cho ,
Jaehyun Park  and
Jangmyung Lee
Seungkeun Cho
Affiliation:
Department of Electronic Engineering, Pusan National University, Busan, South Korea
Jaehyun Park
Affiliation:
Department of Electronic Engineering, Pusan National University, Busan, South Korea
Jangmyung Lee*
Affiliation:
Department of Electronic Engineering, Pusan National University, Busan, South Korea
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper proposes a dynamic localization algorithm for a variable speed mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the time-of-flight of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high-speed mobile robot operations, instead of using two active beacons for localization, an active beacon and the predicted path are utilized to localize the mobile robot. This new approach resolves the high-localization error caused by the speed of mobile robot. The performance of this method was verified by comparing it to the conventional method through real-world experiments.

Keywords

Robot localizationPath planningMobile robotsNavigationSensor networks
Type
Articles
Information
Robotica , Volume 30 , Issue 4 , July 2012 , pp. 681 - 690
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Donoso-aguirre, F., Bustos-salas, J.-P., Torres-torriti, M. and Guesalaga, A., “Mobile robot localization using the Hausdorff distance,” Robotica 26 (2), 129–141 (2008).CrossRefGoogle Scholar
2.Silveira, G., Malis, E. and Rives, P., “An efficient direct approach to visual SLAM,” IEEE Trans. Robot. 24 (5), 969–979 (2008).CrossRefGoogle Scholar
3.Cobano, J. A., Estremera, J. and Gonzalez de Santos, P., “Location of legged robots in outdoor environments,” J. Robot. Auton. Syst. 56 (9), 751–761 (2008).CrossRefGoogle Scholar
4.Hernandez, S., Morales, C. A., Torres, J. M. and Acosta, L., “A new localization system for autonomous robots,” Proc. IEEE Int. Conf. Robot. Autom. 2, 1588–1593 (2003).Google Scholar
5.Garrido, S., Moreno, L., Blanco, D. and Munoz, M. L., “Sensor-based global planning for mobile robot navigation,” Robotica 25 (2), 189–199 (2007).CrossRefGoogle Scholar
6.Gonzalez, J., Blanco, J. L., Galindo, C., Ortiz-de-Galisteo, A., Fernandez-Madrigal, J. A., Moreno, F. A. and Martinez, J. L., “Mobile robot localization based on ultra-wide-band ranging: A particle filter approach,” J. Robot. Auton. Syst. 57 (5), 496–507 (2009).CrossRefGoogle Scholar
7.Milanés, V., Naranjo, J. E., González, C., Alonso, J. and de Pedro, T., “Autonomous vehicle based in cooperative inertial systems,” Robotica 26 (5), 627–633 (2008).CrossRefGoogle Scholar
8.Lin, H.-H. and Tsai, C.-C., “Improved global localization of an indoor mobile robot via fuzzy extended information filtering,” Robotica 26 (2), 241–254 (2007).CrossRefGoogle Scholar
9.Wolf, J., Burgard, W. and Burkhardt, H., “Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization,” IEEE Trans. Robot. 21 (2), 208–216 (2005).CrossRefGoogle Scholar
10.Bleser, G. and Stricker, D., “Advanced tracking through efficient image processing and visual-inertial sensor fusion,” J. Comput. Graph. 33 (1), 59–72 (2009).CrossRefGoogle Scholar
11.Lee, D. and Chung, W., “Discrete status-based localization for indoor service robots,” IEEE Trans. Ind. Electron. 53 (5), 1737–1746 (2006).CrossRefGoogle Scholar
12.Bonci, A., Ippolit, G., La Manna, A. and Longhi, S., “Video data validation by sonar measures for robot localization and environment feature estimation,” Robotica 27 (5), 653–662 (2008).CrossRefGoogle Scholar
13.Han, S., Lim, H. and Lee, J., “An efficient localization scheme for a differential-driving mobile robot based on RFID system,” IEEE Trans. Ind. Electron. 54 (6), 3362–3369 (2007).CrossRefGoogle Scholar
14.Krammer, P. and Schweinzer, H., “Localization of object edges in arbitrary spatial positions based on ultrasonic data,” IEEE Sensors J. 6 (1), 203–210 (2006).CrossRefGoogle Scholar
15.Lazarus, S. B., Ashokaraj, I., Tsourdos, A., Zbikowski, R., Silson, P. M. G., Aouf, N. and White, B. A., “Vehicle localization using sensors data fusion via integration of covariance intersection and interval analysis,” IEEE Sensors J. 7 (9), 1302–1314 (2007).CrossRefGoogle Scholar
16.Vossiek, M., Urban, A., Max, S. and Gulden, P., “Inverse synthetic aperture secondary radar concept for precise wireless positioning,” IEEE Trans. Microw. Theory Tech. 55 (11), 2447–2453 (2007).CrossRefGoogle Scholar
17.Lima, P.U., “A bayesian approach to sensor fusion in autonomous sensor and robot networks,” IEEE Instrum. Meas. Mag. 10 (3), 22–27 (2007).CrossRefGoogle Scholar
18.Loevsky, I. and Shimshoni, I., “Reliable and efficient landmark-based localization for mobile robots,” J. Robot. Auton. 58 (5), 520–528 (2010).CrossRefGoogle Scholar
19.Kim, S.-B., Lee, J. and Lee, I. O., “Precise Indoor Localization System for a Mobile Robot Using Auto Calibration Algorithm,” In: The 13th International Conference on Advanced Robotics, South Korea (2007) pp. 206–211.Google Scholar
20.Han, S., Choi, B. and Lee, J., “A precise curved motion planning for a differential driving mobile robot,” J. Mechatronics 18 (9), 486–494 (2008).CrossRefGoogle Scholar
21.Park, J., Choi, M., Zu, Y. and Lee, J., “Indoor lacalization system in a multi-block workspace,” Robotica 28 (3), 397–403 (2010).CrossRefGoogle Scholar
22.Vorst, P. and Zell, A., “Fully Autonomous Trajectory Estimation with Long-Range Passive RFID,” In: Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, Alaska (May 3–8, 2010) pp. 1867–1872.Google Scholar