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A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots

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Abstract

A relatively new area of research and development is Swarm Robotics. It is a part of the swarm intelligence field. In the proposed paper, we shall use swarm robotics in the field of defense and security, particularly for the problem of counter-improvised explosive device (IED) operations. The biggest problem in this regard is to physically detect the IEDs. We propose the use of a swarm of autonomous robots which shall be moving through the search space to collectively detect IEDs in a relatively lesser span of time with greater reliability. Since the robots are autonomous, there will not be any human contact involved, thus distancing humans from any potential IEDs or hazardous environments. The robot hardware shall be robust and able to traverse different kinds of terrains or even water bodies. A major problem of decision making for autonomous robots is localization of the robots towards the origin. Localization deals with finding its Cartesian coordinates and direction in the given coordinate system. For effective autonomous navigation of a robot, finding the position of the robot is essential at every point of time. Particle swarm optimization (PSO) is a useful method for population based global search. The proposed algorithm is an extension of micro-particle swarm optimization (µPSO) for Simultaneous Localization and Mapping. The effectiveness of this method is estimated by comparing its results with the traditional PSO and µPSO.

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References

  1. 1.

    Mostaghim, S., Steup, C., & Witt, F. (2016). Energy aware particle swarm optimization as search mechanism for aerial micro-robots. In IEEE symposium series on computational intelligence (SSCI).

  2. 2.

    Bielecki, Z., Janucki, J., Kawalec, A., Mikolajczyk, J., Palka, N., & Pasternak., M., et al. (2012). Sensors and systems for the detection of explosive devices—an overview. Metrology and Measurement Systems, X1X, 3–28.

  3. 3.

    Kessentini, S., & Barchiesi, D. (2015). Particle swarm optimization with adaptive inertial weight. International Journal of Machine Learning and Computing, 5(5), 368–373.

  4. 4.

    Kaveh, A. (2017). Particle swarm optimization. Advances in Metaheuristic Algorithms for Optimal Design of Structures, 11–43.

  5. 5.

    Li, Y., Zhan, Z. H., Lin, S., Zhang, J., & Luo, X. (2015). Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Information Sciences Elsevier, 293, 370–382.

  6. 6.

    Jiao, B., Lian, Z., & Gui, X. (2008). A dynamic inertia weight particle swarm optimization algorithm. Chaos, Solitons and Fractals,37, 698–705.

  7. 7.

    Billah, M. M., Khan, R., Ahmed, M., & Shafie, A. (2013). Reconnaissance mission: Development of an algorithm for indoor localisation system with collaborative multi-robot. In Proceedings of the world congress on engineering 2013 (Vol. 3).

  8. 8.

    Mullen, R. J., Barman, S., & Remagnino, P. (2011). Towards autonomous robot swarms for multi-target localization and monitoring with applications to counteried operations. International Journal of Intelligent Defence Support Systems,4(1), 87–107.

  9. 9.

    Cimen, E. B. (2014). Air combat with particle swarm optimization and genetic algorithm. Journal of Aeronautics and Space Technologies,7, 25–35.

  10. 10.

    Yang, Y., Li, B., & Ye, B. (2016). Wireless sensor network localization based on pso algorithm in nlos environment. In 8th international conference on intelligent human-machine systems and cybernetics.

  11. 11.

    Buenfil, J. R. & Ramirez-Marquez, J. (2016). Countering improvised explosive devices with adaptive sensor networks. In 2016 IEEE symposium on technologies for homeland security (HST).

  12. 12.

    Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S., & Abraham, A. (2011). Inertial weight strategies in particle swarm optimization. In Third world congress on nature and biologically inspired computing.

  13. 13.

    Sim, & Poh, P. (2007–2012). Using wireless sensor networks in improvised explosive device detection. In Naval Postgraduate School (U.S.).

  14. 14.

    Patil, M., Abukhalil, T., Patel, S., & Sobh, T. (2016). Ub robot swarm-design, implementation, and power management. In 12th IEEE international conference on control automation (ICCA).

  15. 15.

    Florez, J., & Parra, C. (2016). Review of sensors used in robotics for humanitarian demining application.

  16. 16.

    Gonzalez-Calabuig, A., Ceto, X., & del Valle, M. (2016). Electronic tongue for nitro and peroxide explosive sensing. Talanta,153, 340–346.

  17. 17.

    Patil, M. D., & Abukhalil, T. (2014). Design and implementation of heterogeneous robot swarm. In ASEE 2014 zone I conference.

  18. 18.

    Lee, H. C., Park, S., Choi, J. S., & Lee, B. H. (2009). Fastslam: An improved fastslam framework using particle swarm optimization. In IEEE international conference on systems, man, and cybernetics.

  19. 19.

    El-Abd, M. (2009). Preventing premature convergence in a pso and eda hybrid. In IEEE congress on evolutionary computation.

  20. 20.

    Monfredo, C., & Sahin, F. (2015). Simultaneous localization and mapping using a micro-particle swarm optimization. In 10th system of systems engineering conference (SoSE).

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Correspondence to V. Hemalatha.

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Bakhale, M., Hemalatha, V., Dhanalakshmi, S. et al. A Dynamic Inertial Weight Strategy in Micro PSO for Swarm Robots. Wireless Pers Commun 110, 573–592 (2020) doi:10.1007/s11277-019-06743-x

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Keywords

  • IED
  • Counter-IED
  • Micro-particle swarm optimization
  • Simultaneous localization and mapping