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An Improved Particle Swarm Optimization Method for Motion Planning of Multiple Robots

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Distributed Autonomous Robotic Systems

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 83))

Abstract

Multi robot motion planning is a challenging problem in the robotics field due to its complexity and high computational costs induced by the number of robots. In this paper a new heuristic method is presented for solving this problem through a decentralized approach with global coordination. The method is based on a new improved variant of the Particle Swarm Optimization (PSO) metaheuristic, which serves as a global planner. Alternatively, for local planning and avoiding obstacles in narrow passages, the Probabilistic Roadmap Method (PRM) is employed. The global and local planners act sequentially until all robots reach their goals. The algorithm iteratively and simultaneously minimizes two main objectives, shortness and smoothness of the paths. The proposed algorithm is simulated and compared with the standard (basic) PSO, as well as the standard Probabilistic Roadmap methods. The experimental results show a meaningful advantage of the new method regarding computational time and path quality.

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References

  1. Lozano-Perez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM 22, 560–570 (1979)

    Article  Google Scholar 

  2. Warren, C.W.: Multiple robot path coordination using artificial potential fields. In: Proc. IEEE Int. Conf. Robotics and Automation, pp. 500–505 (1990)

    Google Scholar 

  3. Canny, J.F.: The Complexity of Robot Motion Planning. The MIT press, Cambridge (1988)

    Google Scholar 

  4. Hwang, Y.K., Ahuja, N.: Gross motion planning – A survey. ACM Computing Surveys 24(3), 219–291 (1992)

    Article  Google Scholar 

  5. Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, London (1991)

    Book  Google Scholar 

  6. Chun, L., Zheng, Z., Chang, W.: A decentralized approach to the conflict-free motion planning for multiple mobile robots. In: Proc. IEEE Int. Conf. Rob. Autom., vol. 2, pp. 1544–1549 (1999)

    Google Scholar 

  7. Fujimura, K.: Motion Planning in Dynamic Environments. Springer, New York (1991)

    Book  Google Scholar 

  8. Arai, T., Ota, J.: Motion planning of multiple robots. In: Proc. IEEE Int. Conf. on Intelligent and Robotic Systems, pp. 1761–1768 (1992)

    Google Scholar 

  9. Sanchez-Ante, G., Ramos, F., Frausto, J.: Cooperative Simulated Annealing for Path Planning in Multi-Robot Systems. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS, vol. 1793, pp. 148–157. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  10. Sheng, G., Jie, Z., Hegao, C.: Genetic algorithm based path planning of coordinated multi-robot manipulators. In: Proc. IEEE Int. Conf. on Rob. Intell. Sys. & Signal Proc., pp. 763–767 (2003)

    Google Scholar 

  11. Liu, S., Mao, L., Yu, J.: Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems. In: Proc. IEEE Int. Conf. on Mech. and Autom., pp. 1733–1738 (2006)

    Google Scholar 

  12. Doctor, S., Venayagamoorthy, G.K., Gudise, V.G.: Optimal PSO for collective robotic search applications. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1390–1395 (2004)

    Google Scholar 

  13. Min, H.Q., Zhu, J.H., Zheng, X.J.: Obstacle avoidance with multi-objective optimization by PSO in dynamic environment. In: Proc. IEEE Int. Conf. on Mach. Learning and Cyber., pp. 2950–2956 (2005)

    Google Scholar 

  14. Berman, S., Halasz, A., Kumar, V., Pratt, S.: Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. In: Proc. IEEE Int. Conf. Rob. and Autom., pp. 2318–2323 (2007)

    Google Scholar 

  15. Rigatos, G.G.: Distributed gradient and particle swarm optimization for multi-robot motion planning. Robotica 26(3), 357–370 (2008)

    Article  Google Scholar 

  16. Parhi, D.R., Pothal, J.K., Singh, M.K.: Navigation of multiple mobile robots using swarm intelligence”. In: World Congress on Nature and Biologically Inspired Computing, pp. 1145–1149 (2009)

    Google Scholar 

  17. Kim, S.H., Lee, G., Hong, I., Kim, Y.J., Kim, D.: New potential functions for multi robot path planning: SWARM or SPREAD. In: Proc. IEEE/ICCAE, vol. 2, pp. 557–561 (2010)

    Google Scholar 

  18. Pugh, J., Martinoli, A.: Inspiring and modeling multi-robot search with particle swarm optimization. In: Proc. IEEE Swarm Intelligence Symp., pp. 332–339 (2007)

    Google Scholar 

  19. Akat, S.B., Gazi, V., Marques, L.: Asynchronous particle swarm optimization-based search with a multi-robot system: simulation and implementation on a real robotic system. Turkish Journal of Electrical Engineering & Computer Science 18(5), 749–764 (2010)

    Google Scholar 

  20. Hereford, J.M.: A distributed particle swarm optimization algorithm for swarm robotic applications. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1678–1685 (2006)

    Google Scholar 

  21. Hassan, R., Cohanim, B., de Weck, O.: A comparison of particle swarm optimization and the genetic algorithm. In: Proc. 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference (2004)

    Google Scholar 

  22. Matsui, T., Kato, K., Sakawa, M., Uno, T., Matsumoto, K.: Particle swarm optimization for nonlinear integer programming problems. In: Proc. International MultiConference of Engineers and Computer Scientists, pp. 1874–1877 (2008)

    Google Scholar 

  23. Sedighizadeh, D., Masehian, E.: Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering 1(5), 482–499 (2009)

    Google Scholar 

  24. Masehian, E., Sedighizadeh, D.: Multi-objective robot motion planning using a particle swarm optimization model. Journal of Zhejiang University–Science C 11(8), 607–619 (2010)

    Article  Google Scholar 

  25. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  26. Shi, Y., Eberhart, R.C.: Particle swarm optimization with fuzzy adaptive inertia weight. In: Proc. Workshop on Particle Swarm Optimization, Indianapolis, pp. 101–106 (2001)

    Google Scholar 

  27. Fujimura, K.: Path planning with multiple objectives. Journal of IEEE Robotics and Automation Society 3(1), 33–38 (1996)

    Article  Google Scholar 

  28. Kavraki, L., Svestka, P., Latombe, J.C., Overmars, M.: Probabilistic Roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12(4), 566–580 (1996)

    Article  Google Scholar 

  29. Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press, Boston (2005)

    MATH  Google Scholar 

  30. Sanchez, G., Latombe, J.C.: Using a PRM planner to compare centralized and decoupled planning for multi-robot systems. In: Proc. IEEE Int. Conf. on Rob. and Automation, pp. 2112–2119 (2002)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    Ellips Masehian & Davoud Sedighizadeh

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  1. Ellips Masehian
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  2. Davoud Sedighizadeh
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Editors and Affiliations

  1. EPFL ENAC IIE DISAL, Station 2, Lausanne, 1015, Switzerland

    Alcherio Martinoli

  2. EPFL STI IMT LSRO, Station 9, Lausanne, 1015, Switzerland

    Francesco Mondada

  3. 430 UCB, Engineering Drive 1111, Boulder, 80309, Colorado, USA

    Nikolaus Correll

  4. EPFL ENAC IIE DISAL, Station 2, Lausanne, 1015, Switzerland

    Grégory Mermoud

  5. , Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, Georgia, USA

    Magnus Egerstedt

  6. , Mechanical Engineering & Mechanics, Drexel University, Randell 115, Chestnut St. 3141, Philadelphia, 19104, Pennsylvania, USA

    M. Ani Hsieh

  7. , Department of Electrical Engineering and, University of Tennessee, Middle Drive 1520, Knoxville, 37996, Tennessee, USA

    Lynne E. Parker

  8. Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark

    Kasper Støy

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Masehian, E., Sedighizadeh, D. (2013). An Improved Particle Swarm Optimization Method for Motion Planning of Multiple Robots. In: Martinoli, A., et al. Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32723-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-32723-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32722-3

  • Online ISBN: 978-3-642-32723-0

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