Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-13T08:37:36.413Z Has data issue: false hasContentIssue false
  • English
  • Français

World representation and path planning for a mobile robot

Published online by Cambridge University Press:  09 March 2009

E. Palma-Villalon  and
P. Dauchez
E. Palma-Villalon
Affiliation:
Currently graduate student at University of California, Santa Barbara, CA 93106 (USA).
P. Dauchez
Affiliation:
LAMM-USTL, Place E. Bataillon, 34060 Montpellier Cedex, (France).
Get access Rights & Permissions [Opens in a new window]

Summary

This paper is related to the problem of navigation of a mobile robot amidst obstacles. In order to easily take into account any modification of the environment, we propose a very simple representation of the obstacles, based on the use of rectangles, as well as a matrix description of the spatial relationships between the obstacles. We also present a path planner based on a A* algorithm, the features of which are specifically designed for our world of rectangles. The cost function takes into account both the length of the path and the number of turns. Some experimental results and implementation details are also given in this paper.

Keywords

Mobile RobotPath PlanningWorld Representation
Type
Research Article
Information
Robotica , Volume 6 , Issue 1 , January 1988 , pp. 35 - 40
Copyright
Copyright © Cambridge University Press 1988

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.Kajiwara, T. et al. , “Development of a mobile robot for security guard” Proc. of the 15th ISIR 271–278 (09 1985).Google Scholar
2.Raibert, M. et al. , “3D balance using 2D algorithms” Proc. of ISRR (MIT Press Cambridge, MA, 1984).Google Scholar
3.Narikiyo, T. & Ito, M., “Control of a biped locomotion system in a double support phase” Robotica 3, 73–77 (1985).CrossRefGoogle Scholar
4.Glower, J. & Ozguner, U., “Control of a quadruped trot” Proc. of IEEE ICRA 1496–1501 (04, 1986).Google Scholar
5.Lee, W. J. & Orin, D., “The kinematics of legged locomotion over uneven terrain” Proc. of IEEE ICRA 1490–1495 (04, 1986).Google Scholar
6.Tsumura, T., “Survey of automated guided vehicle in a Japanese factory” Proc. of IEEE ICRA 1329–1334 (04, 1986).Google Scholar
7.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots” Proc. of IEEE ICRA 500–505 (03 1985).Google Scholar
8.Thorpe, C., “Path relaxation: path planning for a mobile robot” Proc. of AAAI 318–321 (08, 1984).CrossRefGoogle Scholar
9.Chatila, R., “Path planning and environmental learning in a mobile robot system” European Conference on A.I. Orsay-France (08, 1982).Google Scholar
10.Gaw, D. & Meystel, A., “Minimum-time navigation of an unmanned mobile robot in a 2 1/2 D world with obstacles” Proc. of IEEE ICRA 1670–1677 (04, 1986).Google Scholar
11.Siy, P., “Road map production system for intelligent mobile robot” Proc. of IEEE ICRA 562–570 (03, 1984).Google Scholar
12.Lozano-Perez, T., “Spatial planning: a configuration space approach” IEEE Trans. on Computers C-32, No. 2, 108–120 (02, 1983).CrossRefGoogle Scholar
13.McDermott, D. & Davis, E., “Planning routes through uncertain territory” Artificial Intelligence 22, 107–156 (1984).CrossRefGoogle Scholar
14.Brooks, R., “Solving the find-path problem by good representation of the free space” IEEE Transactions Systems, Man and Cybernetics SMC-13, No. 3, 190–197 (03/04, 1983).CrossRefGoogle Scholar
15.Oomen, B. et al. , “Robot navigation in unknown terrains of convex polygonal obstacles using learned visibility graphs” Proc. of AAAI 1101–1106 (08, 1986).Google Scholar
16.Singh, S. & Wagh, M., “Robot path planning using intersecting convex shapes” Proc. of IEEE ICRA 1743–1748 (04, 1986).Google Scholar
17.Kuan, D. et al. , “Automatic path planning for a mobile robot using a mixed representation of free space” Proc. of IEEE ICRA 70–74 (03 1984).Google Scholar
18.Witkowski, C., “A parallel processor algorithm for robot route planning” Proc. of IJCAI 827–830 (1983).Google Scholar
19.Meystel, A. et al. , “Minimum time path planning for a robot” Proc. of IEEE ICRA 1678–1687 (04, 1986).Google Scholar
20.Elfes, A., “A sonar-based mapping and navigation system” Proc. of IEEE ICRA 1151–11156 (04, 1986).Google Scholar
21.Winston, P., Artificial Intelligence Second edition (Addison-Wesley, New York, 1984).Google Scholar