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An evolutionary and local search algorithm for planning two manipulators motion

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Tasks and Methods in Applied Artificial Intelligence (IEA/AIE 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1416))

Abstract

A method based on the union of an Evolutionary Algorithm (EA) and a local search algorithm for obtaining coordinated motion plans of two manipulator robots is presented. A Decoupled Planning Approach has been used. For this purpose, the problem has been decomposed into two subproblems: path planning, where a collision-free path is found for each robot independently of the other, only considering fixed obstacles; and trajectory planning, where the paths are timed and synchronized in order to avoid collision with the other robot. This paper focuses on the second problem. A method is presented to minimize the total motion time of two manipulators along their paths, avoiding collision regardless of the accuracy of the dynamic model used. A hybrid technique with EA and local search methods has been implemented.

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References

  1. J.C. Latombe, Robot Motion Planning, Klewer Academic Publishers, 1991.

    Google Scholar 

  2. K. Kant and S.W. Zucker, Toward Efficient Trajectory Planning: The Path-Velocity Decomposition. The Int. Journal of Robotics Research, 5 (3), pp 72–89. 1986.

    Google Scholar 

  3. B.H. Lee and C.S.G. Lee, Collision-Free Motion Planning of Two Robots, IEEE Transations on System, Man and Cyb., pp 21–32, Vol. SMC-17, no 1, Jan-Feb 1987.

    Google Scholar 

  4. P.A. O'Donnell and T. Lozano-Pérez, Deadlock-Free and Collision-Free Coordination of Two Robots Manipulators, Proc. of the IEEE I.C.Robotics Automation., pp 484–489, 1989.

    Google Scholar 

  5. Z. Bien and J. Lee, A Minimum-Time Trajectory Planning Method for Two Robots, IEEE Transations on Robotics and Automation, pp 414–418, vol 8, no 3, June 1992.

    Google Scholar 

  6. M.A. Ridao, Interación Automática de Trayectorias Libres de Colisiones para Mú1tiples Robots Manipuladores. Ph. D. Thesis. Universidad de Sevilla. 1995.

    Google Scholar 

  7. 7. M.A. Ridao, J. Riquelme, E.F. Camacho and M. Toro, Coordinated Motion Planning of manipulators by evolution strategies. Proceedings of the Tenth International Conference on Applications of Artificial Intelligence in Engineering. Udine (Italy). Julio 1995.

    Google Scholar 

  8. J. Riquelme, M.A. Ridao, E.F. Camacho and M. Toro, Using genetic algorithm with variable —length individuals for planning two manipulators motion. Proceedings ICANNGA '97. Norwich (Englad) April, 1997.

    Google Scholar 

  9. Z. Michalewicz, Genetic algorithm + Data structure = Evolution programs. Second Edition, Springer-Verlag. 1994.

    Google Scholar 

  10. S.F. Smith, A learning system based on genetic adaptive algorithms. Ph. D. Thesis. University of Pittsburgh. 1980.

    Google Scholar 

  11. Holland J.H., Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  12. Grefenstette J.J., Incorporating problem specific knowledge into genetic algorithms. Genetic Algorithms and simulated annealing, pp. 42–60, Ed. L. Davis, Morgan Kauffmann Publishers, 1987.

    Google Scholar 

  13. Syrjakow M. and H. Szczerbicka, Optimization of Simulation models with REMO. Proceedings of the conference on Modelling and Simulation, pp. 274–281, 1994.

    Google Scholar 

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Angel Pasqual del Pobil José Mira Moonis Ali

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© 1998 Springer-Verlag Berlin Heidelberg

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Ridao, M.A., Riquelme, J., Camacho, E.F., Toro, M. (1998). An evolutionary and local search algorithm for planning two manipulators motion. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_396

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  • DOI: https://doi.org/10.1007/3-540-64574-8_396

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64574-0

  • Online ISBN: 978-3-540-69350-5

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