Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-24T17:12:14.007Z Has data issue: false hasContentIssue false
  • English
  • Français

Localization of a high-speed mobile robot using global features

Published online by Cambridge University Press:  14 October 2010

Seungkeun Cho  and
Jangmyung Lee
Seungkeun Cho
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
Jangmyung Lee*
Affiliation:
School of Electrical Engineering, Pusan National University, Pusan 609-735, Korea
*
*Corresponding author. E-mail: jangmlee@hanmail.net
Get access Rights & Permissions [Opens in a new window]

Summary

A new localization algorithm is proposed for a fast moving mobile robot, which utilizes only one beacon and the global features of the differential-driving mobile robot. It takes a relatively long time to localize a mobile robot with active beacon sensors, since the distance to the beacon is measured based on the traveling time of the ultrasonic signal. When the mobile robot is moving slowly, the measurement time does not yield a high error. At a higher speed, however, the localization error becomes too large for the mobile robot to be located accurately. Therefore, in high-speed mobile robot operations, instead of using two or more active beacons, only one active beacon and the global features of the mobile robot are used to localize the mobile robot. The two global features are the radius and center of the rotational motion for the differential-driving mobile robot, which generally describe the motion of, and are used for the trace prediction of, a mobile robot. In high-speed operations, the localizer finds the intersection point of this predicted trace and the circle, which is centered at the beacon whose radius is the distance between the mobile robot and the beacon. This new approach overcomes the large localization error caused by the high speed of the mobile robot. The performance of the new localization algorithm is verified through experiments with a high-speed mobile robot.

Keywords

LocalizationDifferential-driving mobile robotActive beaconGlobal features
Type
Articles
Information
Robotica , Volume 29 , Issue 5 , September 2011 , pp. 757 - 765
Copyright
Copyright © Cambridge University Press 2010

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.Lee, D. and Chung, W., “Discrete-status-based localization for indoor service robots,” Ind. Electron. IEEE Trans. 53 (5), 1737–1746 (2006).CrossRefGoogle Scholar
2.Talneau, A., Slempkes, S. and Ougazzaden, A., “Multisensor fusion for simultaneous localization and map building,” Robot. Autom. IEEE Trans. 17 (6), 908–914 (2001).Google Scholar
3.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
4.Tsai, C.-C., “A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements,” Ind. Electron. IEEE Trans. 47 (5), 1399–1404 (1998).Google Scholar
5.Borenstein, J. and Feng, L., “Measurement and correction of systematic odometry errors in mobile robots,” Robot. Autom. IEEE Trans. 12, 869–880 (1996).CrossRefGoogle Scholar
6.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
7.Wolf, J., Burgard, W. and Burkhardt, H., “Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization,” Robot. IEEE Trans. 21 (2), 208–216 (2005).CrossRefGoogle Scholar
8.Chenavier, F. and Crowley, J., “Position Estimation for a Mobile Robot Using Vision and Odometry”, IEEE International Conference on Robotics and Automation (1992) pp. 2588–2593.Google Scholar
9.Huang, J. and Tan, H.-S., “A low-order DGPS-based vehicle positioning system under urban environment,” Mechatronics IEEE/ASME Trans. 11 (5), 567–575 (2006).CrossRefGoogle Scholar
10.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
11.Chen, H., Sun, D. and Yang, J., “Global localization of multirobot formations using ceiling vision SLAM strategy,” Mechatronics 19 (5), 618–628 (Aug. 2009).CrossRefGoogle Scholar
12.Cho, S. K., Shin, S. C. and Lee, J. M., “A Dynamic Localization Algorithm for Mobile Robots using the iGS System,” Proceedings of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2008), pp. 734–739.Google Scholar
13.Yun, J. M., Kim, S. B. and Lee, J. M., “Robust positioning a mobile robot with active Beacon sensors,” Knowl.-Based Int. Inf. Eng. Syst. Part I, 890–897 (2006).CrossRefGoogle Scholar
14.Kim, S. B. and Lee, J. M., “Precise indoor localization system for a mobile robot using auto calibration algorithm,” Korean Robot. Soc. 2 (1), 40–47 (2007).Google Scholar
15.Manolakis, D. E., “Efficient solution and performance analysis of 3-D position estimation by trilateration,” IEEE Trans. Aerosp. Electron. Syst. 32, 1239–1248 (1996).CrossRefGoogle Scholar
16.Ghidary, S. S., Tani, T., Takamori, T. and Hattori, M., “A New Home Robot Positioning System (HRPS) Using IR Switched Multiultrasonic Sensors,” Proceedings of the IEEE Conference on Systems, Man, and Cybernetics 1999 (SMC 1999), vol. 4 (1999) pp. 737–741.Google Scholar
17.Lee, J. M., Son, K., Lee, M. C., Choi, J. W., Han, S. H., Lee, M. H., “Localization of a mobile robot using the image of a moving object,” Ind. Electron. IEEE Trans. 50 (3), 612–619 (2003)Google Scholar
18.Dixon, W. E., Dawson, D. M., Zergeroglu, E. and Behal, A., “Nonlinear Control of Wheeled Mobile Robots,” In: Lecture Notes in Control and Information Sciences, vol. 262 (Springer Verlag, 2001).Google Scholar
19.Chang, W.-C. and Lee, S.-A., “Autonomous Vision-Based Pose Control of Mobile Robots with Tele-Supervision,” IEEE International Conference on Control Applications (2004), pp. 1049–1054.Google Scholar
20.Li, T.-H. S., Chang, S.-J. and Tong, W., “Fuzzy target tracking control of autonomous mobile robots by using infrared sensors,” IEEE Trans. Fuzzy Syst. 12 (4), 491–501 (2004).CrossRefGoogle Scholar
21.Han, S. S., Choi, B. S. and Lee, J. M., “A precise curved motion planning for a differential driving mobile robot,” Mechatronics 18 (9), 1–9 (2008).CrossRefGoogle Scholar
22.Wei, S. and Zefran, M., “Smooth Path Planning and Control for Mobile Robots,” IEEE Conference on Networking, Sensing and Control (2005), pp. 894–899.Google Scholar
23.Chang, W.-C. and Lee, S.-A., “Autonomous Vision-Based Pose Control of Mobile Robots with Tele-Supervision,” IEEE International Conference on Control Applications (2004), pp. 1049–1054.Google Scholar
24.Han, S., Lim, H. S. and Lee, J. M. “An efficient localization scheme for a differential-driving mobile robot based on RFID system,” Ind. Electron. IEEE Trans. 54, 1–8 (2007).CrossRefGoogle Scholar
25.Huang, H.-C. and Tsai, C.-C., “Simultaneous tracking and stabilization of an omnidirectional mobile robot in polar coordinates: a unified control approach,” Robotica 27 (3), 447–458 (May 2009).CrossRefGoogle Scholar