Abstract
A method based on genetic algorithms for obtaining coordinated motion plans of manipulator robots is presented. A decoupled planning approach has been used; that is, the problem has been decomposed into two subproblems: path planning and trajectory planning. This paper focuses on the second problem. The generated plans minimize the total motion time of the robots along their paths. The optimization problem is solved by evolutionary algorithms using a variable-length individuals codification and specific genetic operators.
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References
K. Kant and S. W. Zucker. Toward efficient trajectory planning: The path-velocity decomposition. The Int. J. of Robotics Research, 5(3):72–89, 1986.
J. C. Latombe. Robot Motion Planning. Kluwer Academic Publishers, 1991.
B. H. Lee and C. S. G. Lee. Collision-free motion planning of two robots. IEEE Trans, on SMC, 17(1):21–32, Jan-Feb 1987.
P.A. O’Donnell and T. Lozano-Perez. Deadlock-free and collision-free coordination of two robots manipulators. In Proc. of the IEEE Int. Conf. on Robotics and Automation, pages 484–489, 1989.
M. A. Ridao. Generacion Automtica de Trayectorias Libres de Colisiones para Multiples Robots Manipuladores. PhD thesis, Universidad de Sevilla, 1995.
M. A. Ridao, J. Riquelme, E. Camacho, and M. Toro. Coordinated motion planning of manipulators by evolution strategies. In Proc. of the 10th Int. Conf. on Applications of AI in Engineering. Udine, 1995.
S. F. Smith. A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, University of Pittsburgh, 1980.
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© 1998 Springer-Verlag Wien
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Riquelme, J., Ridao, M.A., Camacho, E.F., Toro, M. (1998). Using Genetic Algorithms with Variable-length Individuals for Planning Two-Manipulators Motion. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_6
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_6
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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