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The Movement of Swarm Robots in an Unknown Complex Environment

  • Quoc Bao Diep
  • Ivan Zelinka
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

This paper presents a method for swarm robots catching multiple moving targets without colliding any dynamic obstacles and other robots in an unknown complex environment. An imaginary map, including multi-layers corresponding to the number of robots, is built in which the starting position, the target, the obstacles, and the robot denoted by the highest position, the lowest position, the small hills, and the spherical ball on the map. The PSO algorithm was proposed to lead the robot to move on the map toward the given targets safely. Simulation results are also presented to show the feasibility of the method.

Keywords

Swarm robot Particle swarm optimization Obstacle avoidance Path planning 

Notes

Acknowledgment

The following grants are acknowledged for the financial support provided for this research: Grant of SGS 2018/177, VSB-Technical University of Ostrava.

References

  1. 1.
    Deepak, B., Parhi, D.R., Raju, B.: Advance particle swarm optimization-based navigational controller for mobile robot. Arab. J. Sci. Eng. 39(8), 6477–6487 (2014)CrossRefGoogle Scholar
  2. 2.
    Diep, Q.B., Zelinka, I.: An algorithm for swarm robot to avoid multiple dynamic obstacles and to catch the moving target. In: XIIIth International Symposium Intelligent Systems, INTELS18, St. Petersburg, Russia (2018, in print, accepted)Google Scholar
  3. 3.
    Diep, Q.B., Zelinka, I.: Obstacle avoidance for swarm robot based on self-organizing migrating algorithm. In: The 2018 IEEE Symposium Series on Computational Intelligence (2018, in print, accepted)Google Scholar
  4. 4.
    Hossain, M.A., Ferdous, I.: Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. Robot. Auton. Syst. 64, 137–141 (2015)CrossRefGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. Perth, Australia. IEEE Service Center, Piscataway (1995)Google Scholar
  6. 6.
    Montiel, O., Orozco-Rosas, U., Sepúlveda, R.: Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst. Appl. 42(12), 5177–5191 (2015)CrossRefGoogle Scholar
  7. 7.
    Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011)CrossRefGoogle Scholar
  8. 8.
    Rashid, A.T., Ali, A.A., Frasca, M., Fortuna, L.: Path planning with obstacle avoidance based on visibility binary tree algorithm. Robot. Auton. Syst. 61(12), 1440–1449 (2013)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Gong, D.W., Zhang, J.H.: Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103, 172–185 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Quoc Bao Diep
    • 1
  • Ivan Zelinka
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceTechnical University of OstravaOstravaCzech Republic

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