Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-12T19:48:53.486Z Has data issue: false hasContentIssue false
  • English
  • Français

Experimental investigation of mapping and navigation based on certainty grids using sonar sensors

Published online by Cambridge University Press:  09 March 2009

Jong Hwan Lim  and
Dong Woo Cho
Jong Hwan Lim
Affiliation:
Department of Mechanical Engineering, Pohang Institute of Science and Technology, P. O. Box 125, Pohang 790-600 (South Korea)
Dong Woo Cho
Affiliation:
Department of Mechanical Engineering, Pohang Institute of Science and Technology, P. O. Box 125, Pohang 790-600 (South Korea)
Get access Rights & Permissions [Opens in a new window]

Summary

A mapping and navigation system based on certainty grids for an autonomous mobile robot operating in unknown environment is described. The system uses sonar range data to build a map of the robot's surroundings. The range data from sonar sensor are integrated into a probability map that is composed of two dimensional grids which contain the probabilities of being occupied by the objects in the environment. A Bayesian model is used to estimate the uncertainty of the sensor information and to update the existing probability map with new range data. The resulting two dimensional map is used for path planning and navigation. In this paper, the Bayesian updating model which was successfully simulated in our earlier work is implemented on a mobile robot and is shown to be valid in the real world by experiment.

Keywords

MappingNavigationMobile RobotBayesian modelPath planning
Type
Research Article
Information
Robotica , Volume 11 , Issue 1 , January 1993 , pp. 7 - 17
Copyright
Copyright © Cambridge University Press 1993

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.Moravec, H.P. and Elfes, A., “High Resolution Maps from Wide Angle Sonar” IEEE International Conference on Robotics and Automation, St. Louis 116–121 (1985).Google Scholar
2.Moravec, H.P., “Sensor Fusion in Certainty Grids for Mobile Robots” Al Magazine 9, No. 2, 61–74 (1988).Google Scholar
3.Moravec, H.P. and Cho, D.W., “A Bayesian Method for Certainty Grids,” AAAl Spring Symposium on Robot Navigation CA57–60 (1989).Google Scholar
4.Cho, D.W., “Certainty Grid Representation for Robot Navigation by a Bayesian Method,” ROBOTICA, Vol. 8 Part 2, 159–165 (1990).CrossRefGoogle Scholar
5.Perez, T. Lozano and Wesley, M.A., “An algorithm for Planning Collision-free Paths Among Polyhedral Obstacles” ACM 22, No. 10, 560–570 (1979).CrossRefGoogle Scholar
6.Thorpe, C.E., “Path Relaxation: Path Planning for a Mobile Robot” American Association for Artificial Intelligence Conference Austin, Texas318–321 (1984).Google Scholar
7.Brooks, R., “Solving the Find-path Problem by Good Representation of Free Space” IEEE Transactions System Man and Cybernetics 13, 190–197 (1983).CrossRefGoogle Scholar
8.Berger, J.O., Statistical Decision Theory and Bayesian Analysis (Springer-Verlag, New York, 1985).CrossRefGoogle Scholar
9.Nayar, S.K., Ikeuchi, K. and Kanade, T., “Surface Reflection: Physical and Geometrical Perspectives” Proc. of DARPA mage Understanding Workshop 1990, Pittsburgh, PA185–212 (September 11–13, 1990).Google Scholar
10.Demming Mobile Robotics Inc., Denning MRU Manual (Wilmington, Mass., USA, 1990).Google Scholar