Abstract
This article introduces a new approach to modeling robot workspace in two dimensions (and its extension to 21/2 dimensions and to mobile objects). The scheme proves to be attractive because it requires less memory space and is execution-efficient. It is very useful for real-time applications.
The grid modeling consists in dividing the space into horizontal and vertical strips whose intersection will form elementary cells. A function giving the access conditions (either a binary one allowing access or nonallowed access, or a more elaborate one including other elements (task, geometries)) is associated to each cell. The size of the adaptive grid will depend on the criterion of the obstacle minimal approach by the robot.
Then a method for path planning is developed. The Lee and A* algorithms being the starting point, the method has been conceived by executing mask operations. So the method requires less memory space and allows an admissible trajectory to be quickly found.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Boschian-Campaner, V., Modélisation de l'environnement par grille adaptative et recherche de chemins pour robots mobiles, Thèse de Doctorat de l'Université de Metz, France (1990).
Canny, J. and Donald, B., Simplified Voronoî diagrams, in I.J. Cox, G.T. Wolfong (Eds),Autonomous Robot Vehicles, Springer-Verlag, pp. 272–278, 1990. Reprinted fromDiscrete and Computational Geometry 3(3), 219–236.
Chatilar, R., Path planning and environment learning in a mobile robot system,Proc. European Conference on Artificial Intelligence, Orsay, France, July 1982.
Crowley, J.L., Navigation for an intelligent mobile robot,IEEE J. Robotics and Automation,RA-1(1), March 1985.
Fryxell, R.C., Navigation planning using Quadtrees,SPIE volume 852 Mobile Robots II (1987).
Hart, P.E., Nilsson, N.J. and Raphael, B., A formal basis for the heuristic determination of minimum cost paths,IEEE Trans. SMC,4(2), 100–107 (1968).
Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots,Intern. J. Robotics Res.,5(1), 90–99 (1986).
Lee, C.Y., An algorithm for path connections and its application,IRE Trans. on Electronic Computers, Sept. 1961, pp. 346–365.
Pruski, A., Multivalue coding: Application to autonomous robots,Robotica,10, 125–133 (1992).
Rogers, D.F.,Algorithmes pour infographie, McGraw-Hill (1983).
Samet, H., Shaffer, C.A. and Nelson, R.C., Recent developments in linear quadtree,Image and Vision Computing,5(3), 187–197 (1987).
Shpitalni, M., Switching function based representation,Ann. CIRP,34, 163–167 (1985).
Singh, J.S. and Wagh, M., Robot path planning using intersection convex shapes analysis and simulation,IEEE J. Robotics and Automation,RA-3(2), April 1987.
Thorpe, C.F., An analysis of interest operator for FIDO, Carnegie Mellon University, Pittsburgh Rapport Technique CMU-RI-TR-83-19.
Tournassoud, P., A strategy for obstacle avoidance and its application to multi-robot systems,Proc. IEEE Int. Conference on Robotics and Automation, San Francisco, April 86, pp. 1224–1229.
Zhu, D. and Latombe, J.C., Constraint reformulation on a hierarchical path planner,IEEE Conference on Robotics and Automation, 1990,Cincinnati Ohio, pp. 1918–1923.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Boschian, V., Pruski, A. Grid modeling of robot cells: A memory-efficient approach. J Intell Robot Syst 8, 201–223 (1993). https://doi.org/10.1007/BF01257995
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF01257995