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Global ultrasonic system with selective activation for autonomous navigation of an indoor mobile robot

Published online by Cambridge University Press:  01 May 2008

Soo-Yeong Yi
Soo-Yeong Yi*
Affiliation:
Department of Electrical Engineering, Seoul National University of Technology, Seoul, South Korea
*
*Corresponding author. E-mail: suylee@snut.ac.kr
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Summary

This paper presents a global ultrasonic system with selective activation algorithm for autonomous navigation of an indoor mobile robot. The global ultrasonic system consists of several ultrasonic transmitters fixed at reference positions in global coordinates and two receivers at moving coordinates of a mobile robot. By activating the ultrasonic transmitters through an radiofrequency (RF) channel, the robot is able to obtain distance information to the reference positions and localize itself in the global coordinates. Due to limitations in signal strength and beam width, the ultrasonic signals from some transmitters may not be delivered to the robot and the ultrasonic data become invalid. In order to improve the effectiveness of the global ultrasonic system, a so-called selective activation algorithm is developed. Based on the current position of the robot, the selective activation calls a proper ultrasonic transmitter and generates valid ultrasonic data at every sampling instant, resulting in faster, more accurate response for self-localization than does simple sequential activation. Path-following control experiments are conducted to verify the effectiveness of the self-localization based on the proposed selective activation algorithm with the global ultrasonic system.

Keywords

Selective activationGlobal ultrasonic systemExtended Kalman filterSelf-localization
Type
Article
Information
Robotica , Volume 26 , Issue 3 , May 2008 , pp. 277 - 283
Copyright
Copyright © Cambridge University Press 2007

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References

1.Singh, S. and Keller, P., “Obstacle detection for high speed autonomous navigation,” Proceedings of IEEE Int. Conf. on Robotics and Automation, Sacramento, USA (September, 1991) pp. 2798–2805.Google Scholar
2.Leonard, J. and Durrant-Whyte, H., Directed Sonar Sensing for Mobile Robot Navigation (Kluwer Academic Publishers, Cambridge, Massachusetts, 1992).CrossRefGoogle Scholar
3.Sala, P., Sim, R., Shokoufandeh, A. and Dickinson, S., “Landmark Selection for Vision Based Navigation,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2004), Sendai, Japan (September, 2004) pp. 3131–3138.Google Scholar
4.Want, R., Hopper, A., Falcao, V. and Gibbons, J., “The Active Badge Location System,” ACM Trans. Inform. Syst. 10 (1), 91–102 (1992).CrossRefGoogle Scholar
5.Haihang, S., Muhe, G., and Kezhong, H., “An integrated GPS/CEPS position estimation system for outdoor mobile robot,” Proceedings of IEEE International Conference on Intelligent Processing Systems Beijing, China (October, 1997) pp. 28–31.Google Scholar
6.Cobb, S., GPS Pseudolite: Theory, Design, and Application, Ph.D. Thesis (Stanford, CA: Stanford University, 1997).Google Scholar
7.Kee, C., Jun, H. and Yun, D., “Indoor Navigation System Using Asynchronous Pseudolites,” J. Navig. 56, pp. 443–455 (2003).CrossRefGoogle Scholar
8.Ko, J., Kim, W. and Chung, M., “A Method of Acoustic Landmark Extraction for Mobile Robot Navigation,” IEEE Trans. Robot. Automat. 12 (6), 478–485 (1996).Google Scholar
9.Leonard, J. and Durrant-Whyte, H., “Mobile Robot Localization by Tracking Geometric Beacons,” IEEE Trans. Robot. Automat. 7 (3), 376–382 (1991).CrossRefGoogle Scholar
10.Kleeman, L., “Optimal Estimation of Position and Heading for Mobile Robots Using Ultrasonic Beacons and Dead-reckoning,” Proceedings of IEEE Conference on Robotics and Automations, Nice, France (May, 1992) pp. 2582–2587.Google Scholar
11.http://www.hexamite.com/Google Scholar
12.Priyantha, N., Miu, A., Balakrishnan, H. and Teller, S., “The Cricket Compass for context-aware mobile applications,” Proceedings of International Conference on Mobile Computing and Networking, Rome, Italy (July, 2001) pp. 1–14.CrossRefGoogle Scholar
13.Yi, S. and Choi, B., “Autonomous Navigation of Indoor Mobile Robot Using Global Ultrasonic System,” Robotica 22 (4), 369–374 (2004).CrossRefGoogle Scholar
14.http://www.acroname.com/robotics/parts/R93-SRF04.htmlGoogle Scholar
15.Welch, G. and Bishop, G., “An Introduction to the Kalman Filter,” Technical Report 95-041 (Chapel Hill, NC: University of North Caroline at Chapel Hill, 1995).Google Scholar
16.Ghidary, S., Tani, T., Takamori, T. and Hattori, M., “A new Home Robot Positioning System (HRPS) using IR switched multi ultrasonic sensors,” Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Tokyo, Japan (October, 1999) pp. 737–741.Google Scholar