Ranked Pareto Particle Swarm Optimization for Mobile Robot Motion Planning

  • D. Wang
  • N. M. Kwok
  • D. K. Liu
  • Q. P. Ha
Part of the Studies in Computational Intelligence book series (SCI, volume 177)


The Force Field (F 2) method is a novel approach for multi-robot motion planning and coordination. The setting of parameters in the (F 2) method, noticeably, can affect its performance. In this research, we present the Ranked Pareto Particle Swarm Optimization (RPPSO) approach as an extension of the basic idea of Particle Swarm Optimization (PSO), which makes it capable of solving multiobjective optimization problems efficiently. In the RPPSO, particles are initiated randomly in the search space; these particles are then evaluated for their qualities with regard to all objectives. Those particles with highly-ranked qualities have preferences to enter the set of Global Best vectors, which stores many currently best solutions found by particles. Thus, particles in RPPSO will search towards many possible directions and the diversity among solutions is well preserved. Ideally, a set of optimal solutions will be found when the termination criterion is met. The effectiveness of the proposed RPPSO is verified in simulation studies. Satisfactory results are obtained for multiobjective optimization problems of multi-robot motion planning in challenging environments with obstacles.


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  1. 1.
    Labtombe, J.C.: Robot motion planning. Kluwer Academic Publisher, Boston (1991)Google Scholar
  2. 2.
    Hwang, Y.K., Ahuja, N.: Gross motion planning - a survey. ACM Computing Surveys 24(3), 219–291 (1992)CrossRefGoogle Scholar
  3. 3.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research 1(5), 90–98 (1986)Google Scholar
  4. 4.
    Connolly, C.I.: Harmonic functions and collision probabilities. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3015–3019 (1994)Google Scholar
  5. 5.
    Ge, S.S., Cui, Y.J.: Dynamic motion planning for mobile robots using potential field method. Autonomous Robot 3(13), 207–222 (2002)CrossRefGoogle Scholar
  6. 6.
    Ren, J., McIsaac, K.A., Patel, R.V., Peters, T.M.: A potential field approach using generalized sigmoid functions. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 37(2), 477–484 (2007)CrossRefGoogle Scholar
  7. 7.
    Wang, D., Liu, D.K., Dissanayake, G.: A variable speed force field method for multi-robot collaboration. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2697–2702 (2006)Google Scholar
  8. 8.
    Wang, D., Kwok, N.M., Liu, D.K., Lau, H., Dissanayake, G.: PSO-tuned F2 method for multi-robot navigation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, pp. 3765–3770 (2007)Google Scholar
  9. 9.
    Kwok, N.M., Ngo, V.T., Ha, Q.P.: PSO-based cooperative control of multiple robots in parameters-tuned formations. In: Proceedings of the IEEE Conference on Automation and Science Engineering (IEEE CASE 2007), pp. 332–337 (2007)Google Scholar
  10. 10.
    Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the First International Conference on Genetic Algorithms: Genetic Algorithms and their Applications, pp. 93–100 (1985)Google Scholar
  11. 11.
    Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)Google Scholar
  12. 12.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the 1994 Congress on Evolutionary Computation, vol. 1, pp. 82–87 (1994)Google Scholar
  13. 13.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  14. 14.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm, Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)Google Scholar
  15. 15.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results, evolutionary computation  8(1), 173–195 (2000)Google Scholar
  16. 16.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  17. 17.
    Doctor, S., Venayagamoorthy, G.K.: Unmanned vehicle navigation using swarm intelligence. In: Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 249–253 (2004)Google Scholar
  18. 18.
    Chen, X., Li, Y.: Smooth path planning of a mobile robot using stochastic particle swarm optimization. In: Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, pp. 1722–1727 (2006)Google Scholar
  19. 19.
    Kwok, N.M., Ha, Q.P., Fang, G.: Motion coordination for construction vehicles using swarm intelligence. International Journal of Advanced Robotic Systems 4(4), 469–476 (2007)Google Scholar
  20. 20.
    Wang, D.L., Liu, D.K., Wu, X., Tan, K.C.: A force field method for robot navigation. In: Proceedings of the Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (2005)Google Scholar
  21. 21.
    Liu, D.K., Wang, D., Dissanayake, G.: A force field method based multi-robot collaboration. In: Proceedings of the IEEE International Conference on Robotics, Autonomous & Mechatronics, pp. 662–667 (2006)Google Scholar
  22. 22.
    Mobile Robot Bases Specifications, ActivMedia Robotics,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • D. Wang
    • 1
  • N. M. Kwok
    • 1
  • D. K. Liu
    • 1
  • Q. P. Ha
    • 1
  1. 1.ARC Centre of Excellence for Autonomous Systems, Faculty of EngineeringUniversity of TechnologySydneyAustralia

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