Abstract
A simple effective method for path planning based on a growing self-organizing elastic neural network, enhanced with a heuristic for the exploration of local directions is presented. The general problem is to find a collision-free path for moving objects among a set of obstacles. A path is represented by an interconnected set of processing units in the elastic self organizing network. The algorithm is initiated with a straight path defined by a small number of processing units between the start and goal positions. The two units at the extremes of the network are static and are located at the start and goal positions, the remaining units are adaptive. Using a local sampling strategy of the points around each processing unit, a Kohonen type learning and a simple processing units growing rule the initial straight path evolves into a collision free path. The proposed algorithm was experimentally tested for 2 DOF and 3 DOF robots on a workspace cluttered with random and non random distributed obstacles. It is shown that with very little computational effort a satisfactory free collision path is calculated.
This research was supported by the Fondo Nacional de Ciencia, Tecnologia e Innovacion (Fonacit) under project S1-2001000814.
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References
Amato, N., Wu, Y.: A Randomized Roadmap for Path Manipulation Planning. In: IEEE, International Conference on Robotics and Automation, pp. 113–120 (1996)
Bracho, M., Moreno, J.A.: Heuristic Algorithm for Robot Path Planning based on Real Space Renormalization. In: Monard, M.C., Sichman, J.S. (eds.) SBIA 2000 and IBERAMIA 2000. LNCS (LNAI), vol. 1952, pp. 379–388. Springer, Heidelberg (2000)
Bruce, J., Veloso, M.: Real-time randomized path planning for robot navigation. In: Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2002 (2002)
Behring, C., Bracho, M., Castro, M., Moreno, J.A.: An Algorithm for Robot Path Planning with Cellular Automata. In: Theoretical and Practical Issues on Cellular Automata, pp. 11–19. Springer, Berlin (2000)
Caselli, S., Reggiani, M., Rocchi, R.: Heuristic methods for randomized path planning in potential fields. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 426–431 (2001)
Durbin, R., Willshaw, D.: An analogue approach to the travelling salesman problem using an elastic net method. Nature 326, 689–691 (1987)
Fritzke, B.: Wachsende zellstrukturen - Ein selbstorganisierendes neuronales netzwerk. Arbeits bericht des IMMD Universitaet Erlangen - Nuernberg (1992)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley, Redwood City (1991)
Kavraki, L.E., Latombe, J.C.: Randomized Preprocessing of Configurations Space for Path Planning. In: IEEE, International Conference on Robotics and Automation, pp. 2138–2139 (1994)
Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publisher, Boston (1991)
Latombe, J.C.: Motion Planning: A Journey of Robots, Molecules, Digital Actors and Other Artifacts. Journal of Robotics Research, Especial Issue on Robotics at the Millenium - Part I 18, 1119–1128 (1999)
Pérez, L.: Spatial planning: A Configuration Space Approach. IEEE Transactions on Computers C-32(2), 108–120 (1983)
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Moreno, J.A., Castro, M. (2005). Heuristic Algorithm for Robot Path Planning Based on a Growing Elastic Net. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_44
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DOI: https://doi.org/10.1007/11595014_44
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