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Obstacle Avoidance for Multi-agent Path Planning Based on Vectorized Particle Swarm Optimization

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Intelligent and Evolutionary Systems

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 8))

Abstract

This paper deals with an approach to path planning by obstacle avoidance for multi-agent systems. An effective framework is presented based on the Particle Swarm Optimization (PSO) method; an evolutionary computation (EC) technique that uses the dynamics of the swarm to search the solutions for the optimization problems. It describes the path replanning technique and obstacle avoidance for autonomous multi-agent systems. A simultaneous replanning concept is incorporated into the path planning to avoid both static and dynamic obstacles. This proposed algorithm reduces the computational time of the path planning. In the dynamic environment, the numerical results show that the Simultaneous Replanning Vectorized Particle Swarm Optimization (SRVPSO) algorithm is effective and also efficient for multi-agent systems.

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References

  1. Wang, D., Wang, H., Liu, L.: Unknown environment exploration of multirobot system with the FORDPSO. Swarm and Evolutionary Computation, vol. 26, pp. 157–174 (2016)

    Google Scholar 

  2. Chang, D. E., Shadden S. C., Marsden, J. E., Olfati-Saber, R.: Collision avoidance for multiple agent systems. Proceedings of the 42nd IEEE conference on Decision and Control, pp. 539–543 (2003)

    Google Scholar 

  3. Francis, S. L. X., Anavatti, S. G., Garratt, M.: Real time cooperative path planning for multi autonomous vehicles. In Advances Computing, Communications and Informatics (ICACCI), International Conference paper on, IEEE, pp. 1053–1057 (2013)

    Google Scholar 

  4. Guo, D. W., Zhou, C. G. and Liu, M.: A hero evolutionary algorithm hybridizing from PSO and GA. In Computational Methods, Springer Netherlands, pp. 1075–1080 (2006)

    Google Scholar 

  5. Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston, MA., ISBN 0-7923-9206-X (1991)

    Google Scholar 

  6. Parker, L. E.: Path planning and motion coordination in multiple mobile robot teams. In Encyclopedia of Complexity and System Science, Meyers, R. A., Ed, pp. 5783–5800. Springer, (2009)

    Google Scholar 

  7. Keron,Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. Conference on Robot Automation, California, pp. 1398–1404, (1991).

    Google Scholar 

  8. Hu, Y., Yang, S. X., A knowledge based genetic algorithm for path planning of a mobile robot. In Proceedings of IEEE Intl. Conference on Robotics and Automation, New Orleans, pp. 4350–4355 (2004)

    Google Scholar 

  9. Yongqiang, B., Wenzhi, X., Hao, F., Jixiang, L., Jing, C.: Obstacle avoidance for multi-agent systems based on stream function and hierarchical associations. In Control Conference (CCC), 31st Chinese, pp. 6363–6367, IEEE (2012).

    Google Scholar 

  10. Clark, C. M., Rock, S. M., Latombe, J-C.: Motion planning for multiple mobile robot systems using dynamic networks. In Robotics and Automation, Proceedings. ICRA’03, IEEE international Conference on, vol. 3, pp. 4222–4227, IEEE (2003)

    Google Scholar 

  11. Liu, F., Narayanan, A.: A human-inspired collision avoidance method for multi-robot and mobile autonomous robots. In International conference on Principles and Practice of Multi-Agent Systems. LNCS, vol. 8291, pp. 181–196. Springer, Heidelberg (2013)

    Google Scholar 

  12. Zhang, Z., Zhao, Z.: A multiple mobile robots path planning algorithm based on A-star and Dijkstra algorithm. International Journal of Smart Home, vol. 8, no. 3, pp. 75–86, (2014)

    Google Scholar 

  13. Peng, J. H., Li, I. H., Chien, Y. H., Hsu, C. C., Wang, W. Y.: Multi-robot path planning based on improved D* Lite Algorithm. In Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference on (pp. 350–353). IEEE. (2015)

    Google Scholar 

  14. Roy, S., Banerjee, D., Majumder, G., Konar, A., Janarthanan, R.: Dynamic obstacle avoidance in multi-robot motion planning using prediction principle. International journal of Advanced computer Engineering & Architecture, vol. 2 no. 2, pp. 269–283 (2012).

    Google Scholar 

  15. Ma, Q., Lei, X., Zhang, Q.: Mobile robot path planning with complex constraints based on the second order oscillating particle swarm optimization algorithm. In Computer Science and Information Engineering, WRI World Congress on, vol. 5 pp. 244–248, IEEE (2009)

    Google Scholar 

  16. Li, T. J., Yuan, G. W., Wang, F. J.: Behavior control of multiple robots exploring unknown environment. In 4th IEEE Conference on Industrial Electronics and Applications, pp. 1877–1882, IEEE (2009)

    Google Scholar 

  17. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering, (2015)

    Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proceedings IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995)

    Google Scholar 

  19. Wang, Y., Chen, P., Jin, Y.: Trajectory planning for an Unmanned ground vehicle group using augmented particle swarm optimization in a dynamic environment. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, san Antonio, TX,USA, pp. 4341–4346 (2009)

    Google Scholar 

  20. Qiaorong, Z., Guochang, G.: Path planning based on improved binary particle swarm optimization algorithm. In IEEE Conference on Robotics, Automation and Mechatronics pp. 462–466, IEEE (2008)

    Google Scholar 

  21. Eberhart, R. C., Shi, Y.: Comparison between genetic algorithm and particle swarm optimization. In Proceedings of the 7th International Conference on Evolutionary Programming VII, pp. 611–616, Springer Berlin Heidelberg (1998)

    Google Scholar 

  22. Wang, L., Liu, Y., Deng, H., & Xu, Y.: Obstacle-avoidance path planning for soccer robots using particle swarm optimization. In IEEE International Conference on Robotics and Biomimetics, pp. 1233–1238, IEEE. (2006)

    Google Scholar 

  23. Mohamed, A. Z., Lee, S. H., Aziz, M., Hsu, H. Y., Ferdous, W. Md.: A proposal on development of intelligent PSO based path planning and image based obstacle avoidance for real multi-agents robotics system application. In Electronic Computer Technology (ICECT), International Conference on, pp. 128–132, IEEE. (2010)

    Google Scholar 

  24. Lu, L., Gong, D.: Robot path planning in unknown environments using particle swarm optimization, Natural Computation, ICNC ‘08. Fourth International Conference on, Vol. 4, 18–20, pp. 422 – 426 (2008)

    Google Scholar 

  25. Saway, A. A. E., Hendawy, Z. M., Shorbagy, M. A. E.: Reference point based TR-PSO for multi-objective environmental/economic dispatch, Scientific Research: Applied Mathematics, vol. 4, no. 5, pp. 803–813 (2013)

    Google Scholar 

  26. Hao, Y., Zu, W., Zhao, Y.: Real-time obstacle avoidance method based on polar coordination particle swarm optimization in dynamic environment. Second IEEE Conference on Industrial Electronics and Applications, pp. 1612–1617 (2007)

    Google Scholar 

  27. Shi, Y., Ebertart, R.: A modified particles swarm optimizer. In Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence., The IEEE International Conference on, pp. 69–73 (1998)

    Google Scholar 

  28. Venter, G., Sobieski, J. S.: Particle swarm optimization. American Institute of Aeronautics and Astronautics (AIAA) Journal, vol. 41, no. 8, pp. 1583–1589 (2003)

    Google Scholar 

  29. Parsopoulos, K. E., Vrahatis, M. N.: Particle swarm optimization and intelligence: advances and applications. Hershey, USA: Information Science Reference, (2010)

    Google Scholar 

  30. Oral, T., Polat, F.: MOD* lite: an incremental path planning algorithm taking care of multiple objectives, IEEE Transactions on Cybernetics, vol. 46, no. 1, pp. 245–257 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Australian Defence Force Academy, The University of New South Wales, Canberra, Australia

    Sumana Biswas, Sreenatha G. Anavatti & Matthew A. Garratt

Authors
  1. Sumana Biswas
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  2. Sreenatha G. Anavatti
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  3. Matthew A. Garratt
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Corresponding author

Correspondence to Sumana Biswas .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    George Leu

  2. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Hemant Kumar Singh

  3. School of Engineering and Information Technology, Australian Defence Force Academy, The University of New South Wales, Canberra, Australian Capital Territory, Australia

    Saber Elsayed

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Biswas, S., Anavatti, S.G., Garratt, M.A. (2017). Obstacle Avoidance for Multi-agent Path Planning Based on Vectorized Particle Swarm Optimization. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-49049-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

  • eBook Packages: EngineeringEngineering (R0)

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