Abstract
Computation of a collision-free path for a movable object among obstacles is an important problem in the fields of robotics. The simplest version of motion planning consists of generating a collision-free path for a movable object among known and static obstacles. In this paper, we introduce a two stage evolutionary algorithm. The first stage is designed to compute a collision-free path in a known environment. The second stage is designed to make on-the-fly updates of the robot current path according to the dynamic environmental modifications. Evolutionary techniques have proven to be useful to both quickly compute a new path and to take advantage of the initial path from the first stage. The tests have been made using simulations and a Lego Mindstorms Robot.
Supported by the Fondecyt Project 1040364
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Latombe, J.C.: Robot Motion Planning. Kluwer Academic Pub., Boston (1991)
Nilsson, N.J.: A mobile automanton: an aplication of artificial intelligence techniques. In: International Joint Conference on Artificial Intelligence, pp. 509–520 (1969)
Ahuja, N., Ahuja, Y.K.: Gross Motion Planning. ACM Computing Surveys 24(3), 219–291 (1992)
Zalama, E.: Arquitectura Neuronal no supervisada para el control de un robot móvil en entornos no estacionarios, Universidad de Valladolid, España (1995)
Xiao, J., Michalewicz, Z.: Adaptive Evolutionary Planner/Navigator for Mobile Robots. IEEE Transactions on Evolutionary Computation 1(1), 18–28 (1997)
Smierzchalski, R., Michalewicz, Z.: Innovations in Machine Intelligence and Robot Perception. In: Path Planning in Dynamic Enviroments. Springer, Heidelberg (2004)
Elshamli, A., Hussein, A., Areibi, S.: Genetic Algorithm for Dynamic Path Planning, School of Enginnering, University of Guelph, Canada (May 2004)
Elshamli, A., Hussein, A., Areibi, S.: Mobile Robots Path planning Optimization, School of Enginnering, University of Guelph, Canada (2004)
Borenstein, J., Feng, L.: Measurement and Correction of Systematic Odometry Error in Mobile Robot. IEEE Journal of Robotics and Automation 12(6), 869–880 (1996)
Tonouchi, Y., Tsubouchi, T., Arimoto, S.: Fusion of Dead-Recogning Position wiht a workspace model for a mobile robot by Bayesian Inference. In: Int. Conf. on Intelligent Robots and Systems, Munich, Germany, September 1994, pp. 1347–1354 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alfaro, T., Riff, MC. (2005). An On-the-fly Evolutionary Algorithm for Robot Motion Planning. In: Moreno, J.M., Madrenas, J., Cosp, J. (eds) Evolvable Systems: From Biology to Hardware. ICES 2005. Lecture Notes in Computer Science, vol 3637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11549703_12
Download citation
DOI: https://doi.org/10.1007/11549703_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28736-0
Online ISBN: 978-3-540-28737-7
eBook Packages: Computer ScienceComputer Science (R0)